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SAO/NASA Astrophysics Data System 13

2021AandA...652A.114P__Nakariakov_&_Verwichte_2005_Instance_1

Multiple solar missions, such as the Solar Dynamics Observatory (SDO) and the Interface Region Imaging Spectrograph (IRIS), have shown that a diversity of waves occur in the solar atmosphere (Jess et al. 2009; McIntosh et al. 2011; Okamoto & De Pontieu 2011). The various wave types that occur include Alfvén waves (Alfvén 1942). These waves are transverse magnetohydrodynamic (MHD) waves that travel along the magnetic field lines. Alfvén waves were reported to be present in both the photosphere and chromosphere (Srivastava et al. 2017; Baker et al. 2021). As they pass by, they modify the transverse magnetic field and velocity components but, do not alter the gas pressure or the mass density (Nakariakov & Verwichte 2005), at least in the linear limit and in a homogeneous background plasma. A thorough understanding of Alfvén waves is essential because they could be a part of the solution to the major problems of heliophysics, such as the solar coronal heating and wind acceleration (Uchida & Kaburaki 1974; Ofman 2010). Recent theoretical research revealed that Alfvén waves can carry enough energy to heat the solar corona (Yang & Xiang 2016). However, the details of the mechanism(s) of the thermal energy release related to their dissipation remain unknown. One potential candidate for that may be associated with ion–neutral collisions (Soler et al. 2017). Piddington (1956), Osterbrock (1961), and Haerendel (1992) were the first to study ion–neutral collisions, but they did not find that this interaction affects the chromospheric temperature. Ballester et al. (2018) showed that ambipolar diffusion leads to substantial chromospheric heating, and Zaqarashvili et al. (2013) derived a dispersion relation for two-fluid Alfvén waves and confirmed that the damping of Alfvén waves resulting from the ion–neutral collisions is quite significant. Khomenko (2017), based on a two-fluid model, stated that the presence of neutrals affects the solar atmosphere. The effect of ion–neutral interactions is expected to influence the energy balance of the chromosphere. Zaqarashvili et al. (2013) also confirmed that low- and high-frequency photospheric Alfvén waves might not reach the solar corona because ion–neutral collisions damp them very efficiently in the upper chromosphere. According to Song & Vasyliūnas (2011), the rate of Alfvén wave damping varies with magnetic field strength and wave frequency. For a strong magnetic field, wave damping is low. Low-frequency waves are also weakly damped, and so there is a chance to detect low-frequency Alfvén waves in the solar corona under the condition of a strong magnetic field.

2021MNRAS.504.5840F__Eriksen_et_al._2007_Instance_2

The standard cosmological model stands on the shoulders of a fundamental assumption: that the universe is statistically homogeneous and isotropic on the largest scales. This assumption has been thoroughly tested over the last years both with cosmic microwave background (CMB) and Large-scale structure data. In particular, the analysis of CMB data, most notably from the Wilkinson Microwave Anisotropy Probe (WMAP; Bennett et al. 2013) and Planck (Planck Collaboration I 2020) experiments, has not yet provided conclusive evidence for the hypothesis of Cosmological Isotropy (Eriksen et al. 2004, 2007; Hajian, Souradeep & Cornish 2005; Land & Magueijo 2007; Hansen et al. 2009; Samal et al. 2009; see also Planck Collaboration VII 2020 and references therein). Moreover, Galactic foreground contamination or known systematic effects in the data alone can not explain the observed CMB ‘anomalies’, i.e. large-scale deviations from the concordance Lambda cold dark matter (ΛCDM) model (see e.g. Rassat et al. 2014; see Planck Collaboration VII 2020 for a recent overview). Power asymmetry from CMB data has also been a matter of intense debate and scrutiny (Gaztañaga, Fosalba & Elizalde 1998; Eriksen et al. 2007; Lew 2008; Hoftuft et al. 2009; Paci et al. 2010; Axelsson et al. 2013; Shaikh et al. 2019, see also Dai et al. 2013 for a comprehensive discussion and references therein), and evidence has been reported that this could source deviations from isotropy on cosmological scales (Hansen et al. 2009). However, a more recent analysis based on Planck data finds no evidence for such power asymmetry when all scales are taken into account (Quartin & Notari 2015). This is in qualitative agreement with the latest results from the Planck Collaboration analysis (Planck Collaboration VII 2020) where they conclude that the observed power asymmetry is not robust to foreground contamination or systematic residuals. It is important to note that previous analysis have concentrated on quantifying potential deviations from statistical isotropy using a statistical prior. First analyses using WMAPdata looked for the direction of maximal asymmetry in the sky, thus quantifying anisotropy for a given preferred direction (Hansen et al. 2009). In turn, this led to proposing a particular angular distribution of power in the sky to simply capture the observed anisotropy, such as the so-called ‘dipole anisotropy’ modulation (Prunet et al. 2005; Gordon 2007). This same model has been further constrained with Planck data (Planck Collaboration XXIII 2014; Planck Collaboration XVI 2016; Planck Collaboration VII 2020; Aiola et al. 2015; Mukherjee et al. 2016). Alternatively, a recent analysis (Ho & Chiang 2018) focuses on quantifying possible CMB peak shifts across the sky, finding significant variations, but they attribute this behaviour to possible systematic effects or the solar dipole. Complementary evidence for cosmological anisotropy has been investigated using probes of the low-redshift universe (see Colin et al. 2011; Secrest et al. 2021 and references therein).

2020ApJ...899..147F__Hörst_et_al._2018_Instance_1

Recent observations of transit spectra of hot Jupiter atmospheres show limited spectral modulation due to H2O that has been largely interpreted as the indicator of the presence of aerosols (Barstow et al. 2016; Iyer et al. 2016; Sing et al. 2016; Pinhas et al. 2019). Whether these aerosols are condensate clouds of photochemical organic aerosols or other refractory materials remains unknown. Although thermochemical equilibrium models predict the formation of condensate clouds with various composition in these hot atmospheres (Lecavelier Des Etangs et al. 2008; Lee et al. 2015; Parmentier et al. 2016), recent laboratory works highlighted that photochemistry could strongly affect the composition of exoplanet atmospheres and lead to the formation of aerosols in a variety of conditions, including the ones encountered in hot Jupiters (Hörst et al. 2018; Fleury et al. 2019; He et al. 2019, 2018a, 2018b). These photochemical aerosols could represent another source of opacity to explain some of the observed transit spectra of hot Jupiter atmospheres, e.g., of HD 189733 b (Lavvas & Koskinen 2017). On the other hand, the bulk elemental ratio can also drastically affect the molecular composition of these atmospheres. In the external layers (region with pressure 1 bar) of atmospheres with temperatures higher than 1000 K, carbon preferentially bonds with oxygen to form CO, and the excess of oxygen bonds with hydrogen to form H2O when the C/O ratio is 1. At a higher CO ratio ≥ 1, CO remains an abundant species but the water mixing ratio decreases (Lodders & Fegley 2002; Moses et al. 2013; Venot et al. 2015; Heng & Lyons 2016; Tsai et al. 2017; Goyal et al. 2018; Drummond et al. 2019). For this reason, another explanation for the low spectral modulation due to water observed in some hot Jupiter atmospheres is that these atmospheres have low H2O abundances presumably reflecting high C/O ratios (Madhusudhan et al. 2011; Madhusudhan 2012). However, the existence of such “carbon-rich” exoplanets continues to be debated. The first analysis of the hot Jupiter WASP-12b observations suggested a C/O ratio > 1 (Madhusudhan et al. 2011), but another study found a C/O ratio 1 using another approach (Kreidberg et al. 2015), leaving the question of the C/O ratio in WASP-12b’s atmosphere open. In addition, a recent survey suggests that the carbon enrichment of hot Jupiter atmospheres compared to their host stars may be common, but uncertainties on C/O measurements in exoplanet atmospheres are large and prevent a firm conclusion from being reached (Brewer et al. 2017).

2020MNRAS.494.5134L__Taylor_1922_Instance_1

The gas velocity is unknown since no exact analytic solution for turbulence in a disc – and turbulence in general – are known. However, statistical properties of turbulence can be inferred from laboratory, numerical experiment or theory, and turbulent fluctuations can be modelled using stochastic processes, independently from the origin of the turbulence itself. In a seminal study, Thomson (1987) proved that the only expression of vg that is consistent with Kolmogorov turbulence and the hydrodynamical equations is (16)$$\begin{eqnarray*} \frac{\mathrm{d}v_{\rm g}}{\mathrm{d}t} = -\frac{v_{\rm g}}{t_{\rm e}} + \frac{\sqrt{D}}{t_{\rm e}}\dot{w} , \end{eqnarray*}$$where te denotes the Lagrangian time-scale of the turbulence, D is the turbulent diffusivity (in units m2s−1). w is a Wiener process, such that its derivative is a white noise such that (17)$$\begin{eqnarray*} \left\langle \dot{w} (t) \right\rangle = 0, \end{eqnarray*}$$(18)$$\begin{eqnarray*} \left\langle \dot{w} (t) \, \dot{w} (t^{\prime }) \right\rangle = \delta (t - t^{\prime }) , \end{eqnarray*}$$where δ denotes the Dirac distribution and the notation · > is the expectation operator (see also Sawford 1984; Wilson & Sawford 1996). Equation (16) describes turbulent fluctuations from a Lagrangian point of view (Taylor 1922). From equation (16), the gas velocity can be rewritten (19)$$\begin{eqnarray*} v_{\rm g} = \zeta \left(t,t_{\rm e},D \right) , \end{eqnarray*}$$where ζ is a stationary Ornstein–Uhlenbeck process defined by (20)$$\begin{eqnarray*} \left\langle \zeta (t,t_{\rm e},D) \right\rangle = 0, \end{eqnarray*}$$(21)$$\begin{eqnarray*} \left\langle \zeta (t,t_{\rm e},D) \, \zeta (t^{\prime },t_{\rm e},D) \right\rangle = \frac{D}{2 t_{\rm e}} \rm {e}^{-\frac{\vert t - t^{\prime } \vert }{t_{\rm e}}} . \end{eqnarray*}$$Equation (16) defines a model of turbulence with two parameters, D and te. In discs, te is typically of order one orbital period, since turbulent vortices are stretched out by differential rotation in a few orbits (e.g. Beckwith, Armitage & Simon 2011). From equation (21), D is related to the autocorrelation of the turbulent noise according to (22)$$\begin{eqnarray*} D = 2 \int _{0}^{+\infty } \left\langle v_{\rm g}\left(0 \right) v_{\rm g}\left(t \right)\right\rangle \mathrm{d}t . \end{eqnarray*}$$Equation (22) can alternatively be seen as a definition of the turbulent diffusivity, useful in practice to measure D in numerical simulations. The Wiener–Khinchin theorem ensures that the power spectrum of the turbulent velocity field S(ω) is the Fourier transform of this autocorrelation function, i.e. (23)$$\begin{eqnarray*} S \left(\omega \right) = \frac{1}{2 \pi } \int _{-\infty }^{+\infty } \mathrm{e}^{- i \omega t} \left\langle v_{\rm g}\left(0 \right) v_{\rm g}\left(t \right)\right\rangle \mathrm{d}t = \frac{D}{2\pi \left(1 + \omega ^2 t_{\rm e}^2 \right)} . \end{eqnarray*}$$Thus, in the inertial subrange ($\omega ^2 t_{\rm e}^2 \gg 1$), we have S(ω) ∝ ω−2, whose equivalent in the wavelength space is $\tilde{S}(k)\propto k^{-5/3}$ (Batchelor 1950). From equation (23), the standard deviation of the velocity fluctuation σ is (24)$$\begin{eqnarray*} \sigma ^{2} \equiv \int _{-\infty }^{+\infty } S\left(\omega \right) \mathrm{d} \omega = \frac{D}{2t_{\rm e}} . \end{eqnarray*}$$Physically, equation (24) is a turbulent fluctuation–dissipation theorem.

2019ApJ...871...86K__Friesen_et_al._2014_Instance_1

On the basis of the 1.3 mm continuum image made from the SMA archival data, Nakamura et al. (2012) found conspicuous substructures inside a prestellar core in the Oph B2 region, which had been previously identified as a single core (B2-N5) in single-dish molecular line (N2H+ (J = 1−0)) observations (Friesen et al. 2010). The substructures consist of several small condensations, and their typical mass and size are around 0.05 M⊙ and 500 au, respectively. The mass is comparable to or larger than the local Jeans mass of 0.04 M⊙, and thus the self-gravity of the condensations appears to play an important role in their dynamics. Similarly, Kamazaki et al. (2001) found small condensations of 1000 au scale inside the SM1 core in the Oph A region based on 3 mm dust continuum observations with the Nobeyama Millimeter Array (see also Nakamura et al. 2012; Friesen et al. 2014). The masses of the condensations are around 0.01−0.1 M⊙, comparable to or larger than the local Jeans mass. Recently, Kirk et al. (2017) carried out comprehensive survey observations with ALMA Band 3 toward 60 dense cores that were identified in Oph with SCUBA at the James Clerk Maxwell telescope (JCMT). They found 38 compact emission structures of ∼100 au size within the dense cores. On the other hand, on the basis of 3 mm continuum observations with CARMA, Schnee et al. (2010) found no significant substructures inside prestellar cores in Perseus (see also Dunham et al. 2016), although their target cores are in relatively isolated environments. These observations suggest that internal structures of prestellar cores and their physical properties may strongly depend on cloud environment. It still remains unclear, however, whether or not they are really different between clustered environments and isolated environments. As a step toward a more comprehensive understanding of the star formation process, it is important to characterize better the substructures inside dense cores in cluster-forming regions. In the present paper, we further investigate the internal structures of the Oph B2 region using Cycle 2 ALMA data at a spatial resolution of ∼3″, corresponding to ∼410 au at Oph distance.

2021MNRAS.501.2522J__Mukherjee_&_Paul_2004_Instance_2

GX 301-2 is an HMXB consisting of a highly magnetized (B ∼ 4 × 1012 G, or even larger Doroshenko et al. 2010) pulsar and a B-type hyper-giant star Wray 977 (Vidal 1973; Kaper et al. 1995; Staubert et al. 2019). According to modelling of high-resolution optical spectra, Wray 977 has a mass of 43 ± 10 $\, \mathrm{M}_{\odot }$, a radius of 62 R⊙, and looses mass through powerful stellar winds at a rate of $\sim \! 10^{-5}\, \mathrm{M}_\odot \, {\rm yr}^{-1}$ with terminal velocity of 300 $\rm km\, s^{-1}$ (Kaper, van der Meer & Najarro 2006). The system is highly eccentric (e ∼ 0.46), with an orbital period of ∼41.5 d, and exhibits strong variation of the X-ray flux with orbital phase (Koh et al. 1997; Doroshenko et al. 2010). In particular, periodic outbursts at the orbital phase ∼1.4 d before the periastron passage (Sato et al. 1986), and a fainter one near the apastron passage are observed (Pravdo et al. 1995). The broad-band X-ray spectrum is orbital phase-dependent and can be approximately described as a power law with a high-energy cutoff and a cyclotron resonant scattering feature around 40 keV (Kreykenbohm et al. 2004; Mukherjee & Paul 2004; La Barbera et al. 2005; Doroshenko et al. 2010; Suchy et al. 2012; Islam & Paul 2014; Fürst et al. 2018; Nabizadeh et al. 2019). During the periastron flares, the source exhibits strong variability with an amplitude of up to a factor of 25, reaching a few hundreds mCrab in the energy band of 2–10 keV (e.g. Rothschild & Soong 1987; Pravdo et al. 1995). The flares are accompanied by the variability of the equivalent hydrogen column density ($\rm \mathit{ N}_{\rm H}$) and of the fluorescent iron lines, which is believed to be associated with clumpiness of the stellar wind, launched from the donor star (Mukherjee & Paul 2004). We note the clumpiness in this paper refers to any inhomogeneities in the stellar wind/stream, which are higher density regions, regardless of its specific formation mechanisms. On the other hand, Fürst et al. (2011) reported a long XMM–Newton observation in GX 301-2 around its periastron, which also exhibits systematic variations of the flux and $\rm \mathit{ N}_{H}$ at a time-scale of a few kiloseconds. Several wind accretion models, consisting of stellar winds and a gas stream, were proposed to explain the observed flares (e.g. Haberl 1991; Leahy 1991; Leahy & Kostka 2008; Mönkkönen et al. 2020).

2015ApJ...809...44Z__Jang_et_al._2014_Instance_1

Admittedly, although the prediction success rate of SPM3 is greatly improved compared with the previous SPM2 model, its improvements in the shock TT prediction are limited. Many factors could potentially cause this. On one hand, the sample events used in this study are CMEs from Solar Cycle 23 when SOHO was the only spacecraft tracking their movements in the sky-plane. The CME speed derived in this way is the projected speed, which does not represent the propagation speed of the CME along the Sun–Earth direction. Large uncertainties in VCME restrict further improvements to the TT prediction of the shock. Possible solutions to this restriction could include adopting the CME’s radial speed, which is derived from models (such as cone models) based on single spacecraft observations (Jang et al. 2014). Another solution involves estimating the initial geometry and three-dimensional (3D) speeds of CMEs based on observations from multiple spacecraft (STEREO, SOHO; Kilpua et al. 2012; Gopalswamy et al. 2013; Lee et al. 2013). On the other hand, the input parameters used in this study were obtained when the disturbances propagated near the Sun. Therefore, the lead time of SPM3's prediction is very long, nearly the whole TT of the disturbance from the Sun to Earth, as the model is analytic and thus requires no running time. Models based on heliospheric image data (STEREO HIs and SMEI) can provide more accurate predictions for arrival times but with shorter lead times (Colaninno et al. 2013; Mishra & Srivastava 2013; Möstl et al. 2014). For example, Webb (2013) applied the Tapping–Howard model (Tapping & Howard 2009) to predict the arrival time at Earth of the 2011 February 15 CME event based on HI and/or SMEI observations, and the corresponding prediction accuracy could be within an hour. However, the prediction’s lead time was only several hours. Kilometric Type II radio burst emission can also be used to track shock dynamics in the inner heliosphere and provide shock arrive time predictions (Corona-Romero et al. 2013; Xie et al. 2013). Further prediction models considering these factors should be developed based on the events of Solar Cycle 24. This is the next goal of our research.

2019MNRAS.490.5478W__Winter,_Booth_&_Clarke_2018c_Instance_1

A growing body of work suggests that planet formation is strongly dependent on the birth environment of the host star. Stars preferentially form in groups (Lada & Lada 2003), and in sufficiently dense environments the evolution of a PPD can be significantly influenced by neighbours (de Juan Ovelar et al. 2012). Close star–disc encounters are one such environmental influence on PPDs that can result in enhanced accretion and hasten disc depletion (Clarke & Pringle 1993; Ostriker 1994; Pfalzner et al. 2005; Olczak, Pfalzner & Spurzem 2006; Bate 2018; Winter et al. 2018a; Cuello et al. 2019). However, the stellar number densities required for tidal truncation are high, and in practice few observed regions satisfy this condition (Winter et al. 2018b, 2019a). The influence of tidal truncation is therefore limited to stellar multiples, either in bound systems (Dai et al. 2015; Kurtovic et al. 2018) or during the decay of higher order multiplicity (Winter, Booth & Clarke 2018c). Since stellar multiplicity does not appear to be strongly dependent on environment (see Duchêne & Kraus 2013, for a review), this suggests that encounters are not an environmental influence, but may set disc initial conditions during the early phases of cluster evolution (Bate 2018). Discs can also be externally depleted via thermal winds driven by far-ultraviolet (FUV) and extreme ultraviolet (EUV) photons from neighbouring massive stars (Johnstone, Fabian & Taylor 1998; Störzer & Hollenbach 1999; Adams et al. 2004; Facchini, Clarke & Bisbas 2016; Haworth et al. 2018; Haworth & Clarke 2019). This process of external photoevaporation dominates over dynamical encounters in observed environments, and can deplete PPDs rapidly for many stars that are born in massive and dense clustered environments (Scally & Clarke 2001; Winter et al. 2018b). Many stars in the solar neighbourhood are born in regions where UV fields are sufficient to significantly shorten disc lifetimes (Fatuzzo & Adams 2008; Winter et al. 2018b), and the fraction of stars born in such environments may be much greater outside of this region, dependent on galactic environment (Winter et al. 2019a). From an observational perspective, Guarcello et al. (2016) report disc survival fractions that decrease with increasing FUV flux in Cygnus OB2 (see also Winter, Clarke & Rosotti 2019b), and Ansdell et al. 2017 find a correlation between the dust mass in PPDs and separation from σ Ori. However, Richert et al. (2015) find no correlation of disc fraction with distance from OB stars. Reconciling these contradictory findings may require appealing to the inefficiency of external photoevaporation at small radii within the disc, dynamical and projection effects, or the stellar age gradient apparent in many star forming regions (Getman et al. 2018).

2019MNRAS.490.5478W__Winter_et_al._2018b_Instance_3

A growing body of work suggests that planet formation is strongly dependent on the birth environment of the host star. Stars preferentially form in groups (Lada & Lada 2003), and in sufficiently dense environments the evolution of a PPD can be significantly influenced by neighbours (de Juan Ovelar et al. 2012). Close star–disc encounters are one such environmental influence on PPDs that can result in enhanced accretion and hasten disc depletion (Clarke & Pringle 1993; Ostriker 1994; Pfalzner et al. 2005; Olczak, Pfalzner & Spurzem 2006; Bate 2018; Winter et al. 2018a; Cuello et al. 2019). However, the stellar number densities required for tidal truncation are high, and in practice few observed regions satisfy this condition (Winter et al. 2018b, 2019a). The influence of tidal truncation is therefore limited to stellar multiples, either in bound systems (Dai et al. 2015; Kurtovic et al. 2018) or during the decay of higher order multiplicity (Winter, Booth & Clarke 2018c). Since stellar multiplicity does not appear to be strongly dependent on environment (see Duchêne & Kraus 2013, for a review), this suggests that encounters are not an environmental influence, but may set disc initial conditions during the early phases of cluster evolution (Bate 2018). Discs can also be externally depleted via thermal winds driven by far-ultraviolet (FUV) and extreme ultraviolet (EUV) photons from neighbouring massive stars (Johnstone, Fabian & Taylor 1998; Störzer & Hollenbach 1999; Adams et al. 2004; Facchini, Clarke & Bisbas 2016; Haworth et al. 2018; Haworth & Clarke 2019). This process of external photoevaporation dominates over dynamical encounters in observed environments, and can deplete PPDs rapidly for many stars that are born in massive and dense clustered environments (Scally & Clarke 2001; Winter et al. 2018b). Many stars in the solar neighbourhood are born in regions where UV fields are sufficient to significantly shorten disc lifetimes (Fatuzzo & Adams 2008; Winter et al. 2018b), and the fraction of stars born in such environments may be much greater outside of this region, dependent on galactic environment (Winter et al. 2019a). From an observational perspective, Guarcello et al. (2016) report disc survival fractions that decrease with increasing FUV flux in Cygnus OB2 (see also Winter, Clarke & Rosotti 2019b), and Ansdell et al. 2017 find a correlation between the dust mass in PPDs and separation from σ Ori. However, Richert et al. (2015) find no correlation of disc fraction with distance from OB stars. Reconciling these contradictory findings may require appealing to the inefficiency of external photoevaporation at small radii within the disc, dynamical and projection effects, or the stellar age gradient apparent in many star forming regions (Getman et al. 2018).

2020MNRAS.494.3413T__Shidatsu_&_Done_2019_Instance_1

The existence of winds is shown by blueshifted absorption lines from highly ionized ions. These are only seen in soft state but not in hard state (Ponti et al. 2012), anticorrelated with the radio jet which is seen in the hard state but not in the soft. This was thought to be evidence that the wind was magnetically driven by the same field as was responsible for the jet, but in a different geometric configuration (Miller et al. 2012). However, in Tomaru et al. (2019, hereafter Paper I) we show instead that thermally driven winds can explain this switch (see also Done, Tomaru & Takahashi 2018; Shidatsu & Done 2019). Thermal driving produces a wind by irradiation from the central source heating the surface of accretion disc up to the Compton temperature ($T_\text{IC} \sim 10^7 \!-\!10^8\, \text{K}$), which is hot enough for its thermal energy to overcome the gravity at large radii. The characteristic radius at which the wind can be launched is called the Compton radius, defined by RIC = μmpGM/kTIC ∼ 105 − 106Rg (Begelman, McKee & Shields 1983). Paper I show the first modern radiation hydrodynamic simulations of thermal (and thermal-radiative) winds designed to investigate the switch in wind properties between the hard and soft states changing illumination spectra. These simulations were tailored to the BHB system H1743−332, where there is Chandra high-resolution data in both states giving detailed spectral information on the wind or its absence (Miller et al. 2012; Shidatsu & Done 2019). They incorporate radiation force on the electrons, both bound and free, as they show that this is important factor driving the escape of the thermal wind in the fairly high Eddington fraction (L/LEdd ∼ 0.2–0.3), fairly low Compton temperature (TIC ∼ 0.1 × 108 K) characteristics of the soft state. The only other modern hydrodynamic simulation of thermal winds (e.g. Luketic et al. 2010; Higginbottom & Proga 2015; Higginbot et al. 2016) has not included radiation pressure, which is important in setting the velocity structure for L ≥ 0.3LEdd as required here (Paper I).

2018MNRAS.475.3419A__Davis_et_al._1999_Instance_2

If we consider for the bulk density the value 4500 kg m−3, which is one of the highest measured in the asteroid population out of those asteroids with good quality of data (see Carry 2012), it will strengthen the hypothesis that Psyche could be an exposed metal core of a differentiated asteroid (Elkins-Tanton et al. 2017). According to the models of asteroid differentiation, the process that led to the formation of Psyche happened very early. Considering Psyche's current diameter, Deff = 226 km (Shepard et al. 2017), the Psyche parent body (PPB) was supposed to be ∼500 km in diameter and have suffered severe ‘hit-and-run’ impact events capable of removing all crust and mantle, exposing the core (Elkins-Tanton et al. 2016). In addition, Psyche should have ∼40 per cent macroporosity, if we assume that it is made of blocks of iron/nickel with a density around 7500 kg m−3. In that case, the core itself was possibly destroyed and re-accumulated, implying a severe collisional history. When an asteroid is disrupted catastrophically, with a remaining mass ≤50 per cent of the initial one, after a collision with another body, an asteroid family is formed. If the collision happened in the Main Belt, a family of asteroid fragments should be in the region of Psyche; however, no family related to Psyche has been found yet (Davis, Farinella & Marzari 1999). One possibility to solve this issue is that the potential Psyche asteroid family was created at an early time, e.g. within the first 500 Myr of Solar system history (Davis et al. 1999). This would allow the family fragments to be ground down by collisional evolution and be unobservable today. The same models show that, even in this case, today there should be several surviving fragments having diameters around 20 km and above the detection limit. There is a lack of primordial asteroid families in the Main Belt (Brož et al. 2013; Spoto, Milani & Knežević 2015), very likely due to the classical methods that are used to identify them. The hierarchical clustering method (HCM) is not sensitive enough to find old and dispersed families, as it searches for asteroids forming compact groups in orbital element space (semi-major axis, eccentricity and inclination). A new approach has been proposed and implemented with success (Walsh et al. 2013; Delbo’ et al. 2017), as it is able to distinguish very old families, having eccentricities and inclinations dispersed in space. Therefore the possibility of the absence of a Psyche family could be due to searching biases. However, this may be an unlikely hypothesis, because A-type asteroids that could represent mantle material (almost pure olivine) from differentiated bodies do not exist extensively in the orbital space related to Psyche, but instead are distributed randomly in the Main Belt (Davis et al. 1999; DeMeo et al. 2015). In order to study this puzzling small body further, NASA is sending a new Discovery Mission to Psyche. The main goal is to get insight into whether it is a core of a parent body and understand the procedures of differentiation, making all the above questions more valid than ever. The alternative theory is that Psyche is a planetesimal that bears primitive unmelted material (Elkins-Tanton et al. 2016).

2019AandA...626A..64H__Grinberg_et_al._2017_Instance_1

Line driven winds are not expected to be smooth flows, but show strong density perturbations or “clumps” (Owocki et al. 1988; Feldmeier et al. 1997; Puls et al. 2006, 2008; Oskinova et al. 2012; Sundqvist & Owocki 2013). In X-ray binaries, thedensity contrast could even be further enhanced by the interaction between the wind and the strong X-rays from the compact object (Blondin 1994; Blondin & Woo 1995; Manousakis & Walter 2011, 2015, and references therein). For Vela X-1 and Cyg X-1 it has been estimated that more than 90% of the wind mass is contained in less than 10% of the wind volume (Sako et al. 1999; Rahoui et al. 2011). When the line of sight to the compact object passes through one of these clumps, X-rays are absorbed by the moderately ionized material in the clump, leading to a so-called dipping event. This is also observed for other sources (Hemphill et al. 2014; Grinberg et al. 2017). During the hard state of Cyg X-1, such short-term dipping events are observed predominantly during the upper conjunction of the black hole, i.e., when the line of sight passes through the densest region of the stellar wind and is most likely to pass through a clump (Li & Clark 1974; Mason et al. 1974; Parsignault et al. 1976; Pravdo et al. 1980; Remillard & Canizares 1984; Kitamoto et al. 1984; Bałucińska-Church et al. 2000; Feng & Cui 2002; Poutanen et al. 2008; Hanke et al. 2009; Miškovičová et al. 2016; Grinberg et al. 2015). The precise structure of the clumps, i.e., their density and ionization structure, is unknown. Most recent 2D simulations of such a stellar wind show a very complex evolution of velocity and density structures with the formation of characteristic small-scale clumps of various shapes embedded in areas with lower density (Sundqvist et al. 2018). Sundqvist et al. (2018) have found a typical clump mass of 1017 g and an average clump size of 1% of the stellar radius at a distance of two stellar radii. These results qualitatively confirm earlier theoretical models (e.g., Oskinova et al. 2012; Sundqvist & Owocki 2013) and observations (e.g., Grinberg et al. 2015). See also the review paper by Martínez-Núñez et al. (2017).

2021ApJ...906...21J__Fabian_et_al._2009_Instance_1

Ricci et al. (2017c, hereafter R17c) recently reported on a study of the relationship between obscuration and accretion rate in a large, relatively unbiased, and complete sample of local AGNs. Specifically, they investigated 836 AGNs with a median redshift of 〈z〉 = 0.037 selected by the hard X-ray (14–195 keV) Swift Burst Alert Telescope (BAT; Gehrels et al. 2004; Barthelmy et al. 2005; Krimm et al. 2013) all-sky survey (Baumgartner et al. 2013; Koss et al. 2017; Oh et al. 2018), which is sensitive to sources with column densities up to NH ≈ 1024 cm−2. Approximately one-half of the sources had robust measurements of column densities, intrinsic X-ray luminosities, and black hole masses, from which R17c was able to show that while unobscured AGNs are seen with Eddington fractions up to the Eddington limit, very few local, obscured AGNs are found with Eddington fractions above approximately 10%. This strengthened earlier results based on smaller samples (e.g., Fabian et al. 2009) and was interpreted as evidence for radiation-pressure-driven AGN feedback (e.g., King 2003; Murray et al. 2005) clearing the immediate BH environment of dusty gas (e.g., Fabian et al. 2006, 2008). For dusty gas (neutral or partially ionized), the effective cross section between matter and radiation (σdust) becomes larger than that between electrons and radiation for ionized gas (σT, for Thompson scattering), due to absorption of radiation by dust. This is given by the Eddington ratio for ionized gas, 1 where Lbol is the bolometric luminosity, LEdd is the Eddington luminosity with MBH the BH mass, and mp is the proton mass. For dusty gas, we use σdust instead of σT in Equation (1), where fEdd is redefined as the effective Eddington ratio (fEdd,dust = fEddσdust/σT). AGNs are strong ionizing sources, but they are fully ionized close to their accretion disks (e.g., Osterbrock 1979; Ballantyne et al. 2001), though the greater than parsec-scale environment starts to be composed of dusty gas (e.g., Kishimoto et al. 2011; Minezaki et al. 2019).

2020ApJ...901...41S__Duval_et_al._2014_Instance_1

Observations have shown that the shape of the Lyα line is diverse. It includes broad damped absorption profiles, P-Cygni profiles, double-peak profiles, pure symmetric emission profiles, and combinations thereof (Kunth et al. 1998; Mas-Hesse et al. 2003; Shapley et al. 2003; Møller et al. 2004; Noll et al. 2004; Tapken et al. 2004; Venemans et al. 2005; Wilman et al. 2005). This variety can be understood through a detailed radiative transfer calculation, which is analytically solvable only for simple cases (e.g., a static, plane-parallel slab by Harrington 1973 and Neufeld 1990, and a static uniform sphere by Dijkstra et al. 2006). Later, numerical algorithms based on Monte Carlo techniques were developed to solve radiative transfer for more general cases. Now theoretical studies mostly rely on them (e.g., Spaans 1996; Loeb & Rybicki 1999; Ahn et al. 2000, 2002; Zheng & Miralda-Escudé 2002; Richling 2003; Cantalupo et al. 2005; Dijkstra et al. 2006; Hansen & Oh 2006; Tasitsiomi 2006; Verhamme et al. 2006, 2015; Laursen et al. 2013; Behrens et al. 2014; Duval et al. 2014; Gronke et al. 2015; Smith et al. 2019; Lao & Smith 2020; Michel-Dansac et al. 2020). Meanwhile, a galaxy model needs to be constructed to perform such a radiative transfer calculation. One can adopt a realistic galaxy model from hydrodynamical simulations. Galaxies from such simulations can be useful for performing a statistical study of Lyα properties, but they cannot be directly used to model individual galaxies in observations. Therefore it would be better to adopt a simple but manageable toy model for the purpose of reproducing observations. One example for such models is a shell model, in which a central Lyα source is surrounded by a constantly expanding, homogeneous, spherical shell of H i medium with dust. Although this shell model has surprisingly well reproduced many observed Lyα line profiles (e.g., Ahn 2004; Schaerer & Verhamme 2008; Verhamme et al. 2008; Schaerer et al. 2011; Gronke et al. 2015; Yang et al. 2016; Gronke 2017; Karman et al. 2017), because of its extreme simplicity and contrivance, there is still room for improvement (e.g., see Section 7.2 in Yang et al. 2016; Orlitová et al. 2018).

2022ApJ...929...11M__Prasad_et_al._2017_Instance_1

In this context, it must also be noted here that these conclusions are specific to the geometry of the GCS model, which is an idealized geometrical figure that has its limitations and constraints (see Thernisien et al. 2009). Regarding the evolution of the legs, the identification of the two separate legs of the CMEs requires observation at the absolute lower heights. Thus the legs can be identified in the K-Cor FOV, while they are not seen in the COR-1 FOV at the same time, as shown in Figure 1; but it should also be noted that despite the promising FOV of K-Cor, the poor image quality due to the challenges faced from it being a ground-based coronagraph makes it difficult to fit (refer to the discussion in Section 2.2). In this regard, the upcoming ADITYA-L1 mission (Seetha & Megala 2017), with the Visible Emission Line Coronagraph (VELC; FOV: 1.05–3 R ⊙; Banerjee et al. 2017; Prasad et al. 2017) on board, and PROBA-3 (FOV : 1.02–3 R ⊙; Renotte et al. 2014), with the giant Association de Satellites pour l’Imagerie et l’Interferométrie de la Couronne Solaire (ASPIICS; Lamy et al. 2017), will provide much better data and hence will help in arriving at much stronger conclusions on the evolution of CME legs. Having said that, it must also be noted that a true estimation of the volume of CME legs will require the CME to be seen FO, as an FO view will help in identifying the inner edges of the CME and hence the volume of its legs. The studied CMEs in this work are all seen FO in the K-Cor FOV (please see Figure 1). Thus, in future, for a larger statistical study, the appearance of the CME (whether FO or EO) should also be considered in the estimation of the volume of the CME legs. Apart from that, around one-third of CMEs have been reported as having a flux-rope morphology (see Vourlidas et al. 2013), which happens to be the bedrock of the GCS model, thus a study of the three separate sections of the flux-rope model of the CME will help us to have a much better understanding of the validity of self-similar expansion, and thus provide more precise constraints to models that study flux-rope initiation and evolution.

2015AandA...584A..75V__Essen_et_al._(2014)_Instance_6

The data presented here comprise quasi-simultaneous observations during secondary eclipse of WASP-33 b around the V and Y bands. The predicted planet-star flux ratio in the V-band is 0.2 ppt, four times lower than the accuracy of our measurements. Therefore, we can neglect the planet imprint and use this band to measure the stellar pulsations, and most specifically to tune their current phases (see phase shifts in von Essen et al. 2014). Particularly, our model for the light contribution of the stellar pulsations consists of eight sinusoidal pulsation frequencies with corresponding amplitudes and phases. Hence, to reduce the number of 24 free parameters and the values they can take, we use prior knowledge about the pulsation spectrum of the star that was acquired during von Essen et al. (2014). As the frequency resolution is 1/ΔT (Kurtz 1983), 3.5 h of data are not sufficient to determine the pulsations frequencies. Therefore, during our fitting procedure we use the frequencies determined in von Essen et al. (2014) as starting values plus their derived errors as Gaussian priors. As pointed out in von Essen et al. (2014), we found clear evidences of pulsation phase variability with a maximum change of 2 × 10-3 c/d. In other words, as an example after one year time a phase-constant model would appear to have the correct shape with respect to the pulsation pattern of the star, but shifted several minutes in time. To account for this, the eight phases were considered as fitting parameters. The von Essen et al. (2014) photometric follow-up started in August, 2010, and ended in October, 2012, coinciding with these LBT data. We then used the phases determined in von Essen et al. (2014) during our last observing season as starting values, and we restricted them to the limiting cases derived in Sect. 3.5 of von Essen et al. (2014), rather than allowing them to take arbitrary values. The pulsation amplitudes in δ Scuti stars are expected to be wavelength-dependent (see e.g. Daszyńska-Daszkiewicz 2008). Our follow-up campaign and these data were acquired in the blue wavelength range. Therefore the amplitudes estimated in von Essen et al. (2014), listed in Table 1, are used in this work as fixed values. This approach would be incorrect if the pulsation amplitudes would be significantly variable (see e.g., Breger et al. 2005). Nonetheless, the short time span of LBT data, and the achieved photometric precision compared to the intrinsically low values of WASP-33’s amplitudes, make the detection of any amplitude variability impossible.

2016MNRAS.461..248S__Munari_et_al._2013_Instance_1

In Sifón et al. (2013), we used the σ–M200 scaling relation of Evrard et al. (2008) to estimate dynamical masses. As discussed in Section 1, the scaling relation of Evrard et al. (2008) was calibrated from a suite of N-body simulations using DM particles to estimate velocity dispersions. However, the galaxies, from which velocity measurements are made in reality do not sample the same velocity distribution as the DM particles. They feel dynamical friction and some are tidally disrupted, which distorts their velocity distribution and biases their dispersion (e.g. Carlberg 1994; Colín et al. 2000). Recent high-resolution hydrodynamical simulations of ‘zoomed’ cosmological haloes have shown that there is a significant difference between the velocity distributions of DM particles and galaxies themselves; whether galaxies (i.e. overdensities of stars in hydrodynamical simulations) or DM subhaloes are used makes comparatively little difference (Munari et al. 2013). Results from state-of-the art numerical simulations depend on the exact definition of a galaxy and the member selection applied, but the current consensus is that galaxies are biased high (i.e. at a given mass the velocity dispersion of galaxies or subhaloes is larger than that of DM particles) by 5–10 per cent with respect to DM particles (Lau et al. 2010; Munari et al. 2013; Wu et al. 2013), translating into a positive 15–20 per cent bias in dynamical masses when using DM particles. This is illustrated in Fig. 5: DM particles are not significantly impacted by either dynamical friction or baryonic physics; therefore, the scaling relations for DM particles are essentially the same for all simulations. In contrast, DM subhaloes are affected by baryons in such a way that including baryonic feedback (most importantly feedback from active galactic nuclei – AGN, but also from cooling and star formation) makes their velocity dispersions much more similar to those of simulated galaxies. This means we can rely on our analysis of the previous section, based on DM subhaloes, to correct the velocity dispersions measured for ACT clusters, and then estimate dynamical masses using predictions obtained either from galaxies or subhaloes. The difference between the Saro et al. (2013) and Munari et al. (2013) galaxy scaling relations depends on the details of the semi-analytic and hydrodynamical implementations used in Saro et al. (2013) and Munari et al. (2013), respectively. The different cosmologies used in the Millenium simulation (in particular, σ8 = 0.9; Springel et al. 2005) by Saro et al. (2013) and the simulations by (Munari et al. 2013, σ8 = 0.8) may also play a role.

2018ApJ...855...23I__Yang_et_al._2014_Instance_1

Cosmic rays (CRs) represent a crucial ingredient in the dynamical and chemical evolution of interstellar clouds. Interaction of CRs with molecular clouds is accompanied by various processes generating observable radiation signatures, such as ionization of molecular hydrogen (see, e.g., Oka et al. 2005; Dalgarno 2006; Indriolo & McCall 2012) and iron (e.g., Dogiel et al. 1998, 2011; Tatischeff et al. 2012; Yusef-Zadeh et al. 2013; Nobukawa et al. 2015; Krivonos et al. 2017), as well as production of neutral pions whose decay generates gamma-rays in the GeV (e.g., Yang et al. 2014, 2015; Tibaldo et al. 2015) and TeV (e.g., Aharonian et al. 2006; Abramowski et al. 2016; Abdalla et al. 2017) energy ranges. Being a unique source of ionization in dark clouds, where the interstellar radiation cannot penetrate, CRs provide a partial coupling of the gas to magnetic field lines, which could slow down or prevent further contraction of the cloud (e.g., Shu et al. 1987). CRs are fundamental to the beginning of astrochemistry because they promote the formation of ions, which can easily donate a proton to elements such as C and O, and thus eventually form molecules containing elements heavier than H (e.g., Yamamoto 2017). Through the ionization of H2 molecules and the consequent production of secondary electrons, CRs are an important heating source of dark regions (e.g., Goldsmith 2001). Their interaction with H2 can also result in molecular excitation, followed by fluorescence producing a tenuous UV field within dark clouds and dense cores (Cecchi-Pestellini & Aiello 1992; Shen et al. 2004; Ivlev et al. 2015a); this UV field can photodesorb molecules from the icy dust mantles and help to maintain a non-negligible amount of heavy molecules (such as water) in the gas phase (e.g., Caselli et al. 2012). Furthermore, CRs can directly impinge on dust grains and heat up the icy mantles, causing catastrophic explosions of these mantles (Léger et al. 1985; Ivlev et al. 2015b) and activating the chemistry in solids (Shingledecker et al. 2017). Finally, CRs play a fundamental role in the charging of dust grains and the consequent coagulation of dust (Okuzumi 2009; Ivlev et al. 2015a, 2016), which is particularly important for the formation of circumstellar disks (e.g., Zhao et al. 2016) and of planets in more evolved protoplanetary disks (e.g., Testi et al. 2014).

2015AandA...578L...8B__Berné_et_al._2009_Instance_1

Gomez’s Hamburger (IRAS 18059-3211; hereafter GoHam) is an A star surrounded by a dusty disk. When first studied by Ruiz et al. (1987), it was classified as an evolved object (post-AGB star) on the basis of its spectral type and the presence of dust. However, all recent studies (Bujarrabal et al. 2008, 2009; Wood et al. 2008; De Beck et al. 2010) clearly indicate that it is a young A star surrounded by a protoplanetary disk. The distance to GoHam is not known precisely, but a value d = 250 ± 50 pc is required to satisfy all the existing observational constraints (Wood et al. 2008; Berné et al. 2009; Bujarrabal et al. 2009). We here adopt this value with the uncertainty. GoHam presents intense CO emission; SMA maps of 12CO and 13CO J = 2 − 1 lines very clearly show the Keplerian dynamics of the disk (Bujarrabal et al. 2008, 2009). The lower limit for the disk mass derived from these CO observations is of about 10-2M⊙, while the mass upper limit is estimated to be ~0.3 M⊙ based on dust emission (Bujarrabal et al. 2008; Wood et al. 2008) and assuming an interstellar dust-to-gas mass ratio of 0.01. Overall, GoHam appears to be similar to isolated Herbig stars (Meeus et al. 2001) such as HD 100546 and HD 169142, in a more massive version, but still smaller than the recently discovered disk around CAHA J23056+6016 (Quanz et al. 2010). GoHam is seen almost perfectly edge on, which offers the possibility to study this class of objects from a new and complementary perspective, in particular, with improved constraints on the vertical structure of the disk. Using a radiative transfer model to predict line emission from a Keplerian flaring disk, Bujarrabal et al. (2009) derived a large-scale description of the physical conditions throughout the disk. After subtracting the model that best fit the observations, these authors found a significant residual emission situated about 1.3′′ (330 ± 70 AU) south of the central star, which they identified as a gas condensation, containing a mass between one and few times that of Jupiter. Hence, this source was proposed to be a candidate protoplanet, possibly resulting from a GI collpapse.

2022AandA...666A..28S__Rutherford_1903_Instance_1

The velocity distribution of the plasma motion is shown in Fig. 12, where the magnitude is scaled with respect to the Alfvén velocity, vA, which is measured based on the magnetic field strength, B0 = 2 G, and the mass density of the equilibrium current sheet, ρc = 2.81 × 10−15 g cm−3. The Alfvén timescale is measured by t A = L ¯ / v A $ t_A = \bar{L}/\mathit{v}_A $ , where, L ¯ $ \bar{L} $ is the unit length of 109 cm. It is evident from Figs. 12a, b and c that the velocity, vx remains localized in the vicinity of the current sheet (y = 0). We note that in line with our initial single-island magnetic field perturbation, we see a pronounced rightward motion in the right half of the domain, and a leftward motion in the left half. We later see typical Petschek-like signatures in the flow fields between islands, especially about the middle x = 0, with super-Alfvénic outflow speeds bounded by slow shocks. Figure 13a represents the evolutionary nature of a current sheet in adiabatic and non-adiabatic conditions. It is clear from the figure that the instantaneous maximum velocity growth for the non-adiabatic case is more rapid than for the adiabatic conditions. The evolutionary behavior of the current sheet configuration due to thermal and tearing instabilities is shown by the black curve in Fig. 13a for plasma-β = 0.2, and a given resistivity value, η = 0.001, while the evolution for different η values are shown in Fig. 13b. As a diagnostic measurement of the instability, we determine the evolution of the instantaneous maximum absolute velocity, |vx|max. Figure 13a shows that this evolution exhibits three distinct phases: (i) the early phase (between t = 0 and 250 s), where the velocity growth occurs exponentially (linearly on the logarithmic-linear scale), which is called the linear growth regime; (ii) the middle phase between t = 250 and 665 s, where the growth rate is slower compared to the linear phase, which is called the Rutherford regime (Rutherford 1903), and (iii) the final phase, which starts at t = 665 s, where the instability suddenly develops in an explosive way, and finally saturates at a later time, which we call the post-Rutherford regime. To infer the evolution rates quantitatively for all the different phases, we calculate the growth rates by scaling them with respect to the Alfvén timescale, tA. We define the growth rate as γ = d(ln(|vx|max))/dt. To estimate the linear growth rate, γlin, we calculate the growth rate in the linear regime by taking the mean value of the slope, which gives γ l i n = 3.76 × 10 − 1 t A − 1 $ \gamma_{\rm lin}=3.76 \times 10^{-1} t_A^{-1} $ . This value is larger by an order of magnitude compared to the studies of the double current sheet problem (Otto & Birk 1992; Zhang & Ma 2011; Akramov & Baty 2017; Paul & Vaidya 2021), where the radiative cooling effect (or other non-adiabatic effects, e.g., thermal conduction) is not incorporated. This implies that the higher linear growth rate can be ascribed to the non-adiabatic effects of the radiative cooling and background heating. This is also in agreement with our own study for a single current layer model reflected in Fig. 13a, where the average growth rate for the adiabatic medium is lower than the non-adiabatic case. Similarly, we estimate the average growth rates for the Rutherford regime (γRuth) and the post-Rutherford regime (γPR) for different resistivity values within the range of η = 0.0001 to 0.005. The velocity evolution for some selected resistivity values are shown in Fig. 13b. This shows that the explosive phase of the evolution starts at later times for higher resistivity values, and converges at the final stage. For a Sweet–Parker type current sheet (where the inverse aspect ratio of the current sheet follows the scaling relation l s / L ∼ S L − 1 / 2 $ l_s/L \sim S_{\mathrm{L}}^{-1/2} $ ), the thickness of the current sheet increases with the resistivity (Loureiro et al. 2007), which reduces the growth rate of the tearing mode when it is normalized with respect to the Alfvén crossing time along the length of the current sheet (in the x-direction in our case). Hence, the explosive phase of the evolution in our simulation starts at later times for higher resistivity values. We estimated the absolute current density, |Jz| (normalized to unity) before the fragmentation stage of the current sheet (t = 214 s) by taking a vertical cut along the y-direction at x = 0 for two different resistivities, η = 0.0001 and 0.001, to confirm that the thickness of the current sheet increases with resistivity (see Fig. 14). The resistivity dependence for the different evolution phases is shown in Fig. 15. Figure 15a shows that γRuth follows a power-law dependence with the resistivity, γRuth ≈ η−0.1, with a correlation coefficient (CC) of −64.1%. The resistivity scaling relation for the post-Rutherford and the entire nonlinear regimes are shown in Figs. 15b and c respectively. We estimate the growth rate scaling relations for the post-Rutherford regime, γPR ≈ η0.03 (with CC = 59.9%), and the entire non-linear regime, γavg ≈ η0.017 (with CC = 66.7%). Previous studies by Zhang & Ma (2011), Akramov & Baty (2017), and Guo et al. (2017 and references therein) have reported the resistivity scaling relation of the non-linear growth rates for the DTM setup in the adiabatic environment, which have larger power-law indices compared to our estimation. Hence, our study infers that the resistivity dependence on the nonlinear growth rates is weaker when the thermal instability reinforces the tearing mode.

2020MNRAS.495..758H__Wang_et_al._2014_Instance_1

In all our simulations independent of the inflow Mach number, the radial velocity dispersion at the filament boundary amounts to about 0.85 times the total equilibrium velocity dispersion of the non-self-gravitational case for which a functional form can be found in the appendix. Thus, we can calculate the theoretical radius and central density of the filament at every line-mass and therefore we can make predictions on the fragmentation length and time-scales of cores forming in an accreting filament using the gravitational fragmentation model. This model was successfully applied to explain several observed core distances (Jackson et al. 2010; Miettinen 2012; Busquet et al. 2013; Beuther et al. 2015; Contreras et al. 2016; Heigl et al. 2016; Kainulainen et al. 2016) however it is not able to explain all observations (André et al. 2010; Kainulainen et al. 2013; Takahashi et al. 2013; Lu et al. 2014; Wang et al. 2014; Henshaw et al. 2016; Teixeira et al. 2016; Kainulainen et al. 2017; Lu et al. 2018; Palau et al. 2018; Williams et al. 2018; Zhou et al. 2019). It predicts that small density perturbations in the linear regime along the filament axis of the form: (32)$$\begin{eqnarray*} \rho (r, x, t) = \rho _0(r) \left(1 + \epsilon \exp (ikx -i\omega t)\right) \end{eqnarray*}$$will grow for values of k where the dispersion relation ω2(k) is negative. Here ρ0 is the unperturbed initial density, k = 2π/λ is the wave vector with λ being the perturbation length, x is the filament axis, ω = 1/τ is the growth rate with τ being the growth time-scale, t the time variable, and ϵ the perturbation strength. The fastest growing, or dominant, fragmentation length scale λdom as well as the growth time-scale of the dominant mode τdom depend on the current line-mass as well as the current central density of the filament and are given by the pre-calculated (Nagasawa 1987) and interpolated values in Fischera & Martin (2012), shown by their Table E.1. We use these values to determine the length scale of the fastest growing mode at every line-mass for the same mass accretion rate but for different inflow Mach numbers as shown in Fig. 10. As one can see, the dominant fragmentation length changes over the evolution of the line-mass. At the boundary values it vanishes to zero and it has a maximum at about fcyl = 0.4. The figure is self-similar for different mass accretion rates, with a lower rate leading to a larger dominant fragmentation length. For a constant accretion rate, the fragmentation length does not vary much for different inflow Mach numbers. Only for large and for very low inflow Mach numbers, the fragmentation length is slightly larger. As the dominant fragmentation length constantly changes as fcyl grows, it is hard to make predictions of what will be the final distance between forming cores. But the curves have a maximum which allows us to make a prediction about the minimum number of cores that will form. For instance, a filament with an inflow Mach number of 4.0 and a length of 0.2 pc will form at least one core. As soon as the first core forms, the further evolution of the filament is also influenced by the gravitational attraction of the core. This makes the formation of additional cores even more unpredictable.

2018MNRAS.473.3810Y__Mitrushchenkov_et_al._2017_Instance_2

The lack of data on inelastic processes due to collisions with neutral hydrogen atoms has been a major limitation on modelling of F-, G- and K-star spectra in statistical equilibrium, and thus to reliably proceeding beyond the assumption of local thermodynamic equilibrium (LTE) in analysis of stellar spectra and the determination of elemental abundances. This problem has been well documented, e.g. see Lambert (1993); Barklem (2016a) and references therein. Significant progress has been made in recent times through detailed full-quantum scattering calculations, based on quantum chemical data, for the cases of simple atoms such as Li, Na, Mg and Ca (Belyaev & Barklem 2003; Barklem, Belyaev & Asplund 2003; Belyaev et al. 2010; Barklem et al. 2010; Belyaev et al. 2012; Barklem et al. 2012; Mitrushchenkov et al. 2017). These calculations have demonstrated the importance of the ionic-covalent curve crossing mechanism leading naturally to charge transfer processes (mutual neutralization and ion-pair production), in addition to excitation and de-excitation processes. The importance of this mechanism has allowed various simplified model approaches to be developed, which may be used in cases where suitable quantum chemistry data are not been available. In particular a semi-empirical model has been employed for Al, Si, Be and Ca (Belyaev 2013a,b; Belyaev, Yakovleva & Barklem 2014b; Yakovleva, Voronov & Belyaev 2016; Belyaev et al. 2016), and a theoretical model based on a two-electron asymptotic linear combinations of atomic orbitals (LCAO) approach, has also been employed for Ca (Barklem 2016b, 2017). Comparisons of the two methods show quite good agreement and reasonable agreement with the full quantum calculations is found, particularly for the most important processes with the largest rates (Barklem 2016b, 2017; Mashonkina, Sitnova & Belyaev 2017; Mitrushchenkov et al. 2017). Thus, the model approaches provide a useful route for obtaining estimates of the rates for these processes for many elements of astrophysical interest.

2021MNRAS.507.1138M__Parfenov_&_Sobolev_2014_Instance_1

Several mechanisms have been proposed to explain the periodicity of methanol masers. van der Walt, Goedhart & Gaylard (2009) and van der Walt (2011) proposed a colliding wind binary (CWB) system for the periodic methanol masers in G9.62+0.20E and other periodic methanol maser sources with similar light curves. Using a more realistic model van den Heever et al. (2019) also showed that the CWB scenario can explain the light curves of the periodic methanol masers in G9.62+0.20E, G22.357+0.066, G37.55+0.20, and G45.473+0.134. Other mechanisms proposed are the pulsational instabilities of very young accreting high-mass stars (Inayoshi et al. 2013), spiral shocks associated with very young binary systems orbiting within a circumbinary disc (Parfenov & Sobolev 2014), periodic accretion in a very young binary system (Araya et al. 2010), and outflows in a binary system (Singh & Deshpande 2012). Maswanganye et al. (2015) evoked an eclipsing binary system involving a high-mass (primary) star and a bloated low-mass (companion) star in explaining the observed periodicity of the methanol masers in G358.460-0.391. While each of the suggested mechanisms may be sufficient for explaining periodicity in individual star-forming regions, the complexity in the nature of massive star-forming environments and the uniqueness of each region, make it difficult to evoke any one of the mechanisms for all cases. In fact, different mechanisms may be responsible for the different observed flare profiles (see van der Walt et al. 2016, for a discussion). However, the fact that it is possible to identify at least two groups of periodic sources (as explained above), each for which the light curves are very similar, suggests that there are some sources for which the underlying mechanism is the same. It is therefore reasonable to argue that there might be other similarities in star-forming regions that host periodic methanol masers with same light curves. In other words, the similarity in light curves might also manifest in other properties of the star-forming regions in addition to the maser emission.

2019AandA...628A.118B__Feruglio_et_al._2017_Instance_2

Ultra-fast outflows (UFOs) of highly ionised gas observed at sub-parsec scales (Reeves et al. 2003; Tombesi et al. 2012) have been proposed as the likely origin of galaxy-wide outflows, interpreted as the result of the impact of UFOs on the ISM (King & Pounds 2015, and references therein). Furthermore, both models and observations of kiloparsec-scale outflows seem to indicate a UFO-ISM interaction in an energy-conserving regime, whereby the swept-up gas expands adiabatically. So far, the co-existence of a massive molecular outflow with a nuclear UFO has been confirmed in a handful of AGNs with LBol  ∼  1044 − 1046 erg s−1 (Tombesi et al. 2015; Feruglio et al. 2015; Longinotti et al. 2015) and in APM 08279+5255 (Feruglio et al. 2017), which is a gravitationally lensed QSO at z  ∼  4 with an estimated intrinsic LBol of a few times 1047 erg s−1 (Saturni et al. 2018). In all these sources the momentum boost (i.e. the momentum flux of the wind normalised to the AGN radiative momentum output, LBol/c) of the UFO is ∼1, while the momentum rate of the molecular outflow is usually ≫1, in qualitative agreement with the theoretical predictions for an energy-conserving expansion (Faucher-Giguère & Quataert 2012; Costa et al. 2014). However, these results are still limited to a very small sample and suffer from large observational uncertainties, mostly due to the relatively low signal-to-noise ratio of the UFO- or outflow-related features confirmed in spectra, or to the limited spatial resolution of sub-millimetre observations. Recent studies increasing the statistics of sources with detection of molecular outflows have widened the range of measured energetics (e.g. García-Burillo et al. 2014; Veilleux et al. 2017; Feruglio et al. 2017; Brusa et al. 2018; Barcos-Muñoz et al. 2018; Fluetsch et al. 2019). These outflows are consistent with driving mechanisms alternative to the energy-conserving expansion, such as direct radiation pressure onto the host-galaxy ISM (e.g. Ishibashi & Fabian 2014; Ishibashi et al. 2018; Costa et al. 2018).

2020ApJ...903L..22T__Vuitton_et_al._2007_Instance_1

While the Loison et al. (2015) CH3C3N model corroborates the upper atmospheric abundance of C4H3N inferred by Vuitton et al. (2007) from the T5 INMS measurements (a factor of 2 higher than those derived from T40 in Vuitton et al. 2019), a large disparity between the photochemical models (and within the ensemble of models produced by Loison et al. 2015) arises in the lower atmosphere due to the poorly constrained C4H3N branching ratios and reaction rate coefficients at temperatures appropriate for Titan. Aside from electron dissociative recombination of C4H3NH+ (Vuitton et al. 2007), neutral production of CH3C3N can occur in a few ways, as found through crossed beam experiments and theoretical and photochemical modeling studies (Huang et al. 1999; Balucani et al. 2000; Zhu et al. 2003; Wang et al. 2006; Loison et al. 2015). First, through the reactions of larger hydrocarbons with CN radicals, 1 2 Similarly, with CCN radicals following their formation through H + HCCN (Takayanagi et al. 1998; Osamura & Petrie 2004) and subsequent reactions with ethylene, 3 or through the chain beginning with acetylene, 4 While both reactions (3) and (4) are found to be equally likely by Loison et al. (2015), the production of CCN via H + HCCN is not well constrained, and the synthesis of CH3C3N through CN radicals (Equations (1) and (2)) are not included in their photochemical model. Additionally, cyanoallene may be produced through reactions (1)–(4) instead of (or in addition to) methylcyanoacetylene. CH3C3N itself may form the protonated species, C4H3NH+, through reactions with the HCNH+ and C2H5+ ions producing HCN and C2H4, respectively (Vuitton et al. 2007). The other mechanism for forming C4H3NH+ is through the combination of HCN and l-C3H3+, though the reaction rate coefficient for this reaction and the abundance of l-C3H3+ are unknown (Vuitton et al. 2007). As such, the production and loss pathways for both C4H3NH+ and CH3C3N require further investigation.

2020ApJ...892..110C__Saladino_et_al._2018_Instance_1

Asymptotic-giant-branch (AGB) stars have a significantly larger size (∼1 au) than their main-sequence (MS) counterparts. They have pulsating atmospheres (Vlemmings et al. 2017; Khouri et al. 2019) and may exhibit variability with long periods ranging from 200 to 1000 days (Mowlavi et al. 2018; Karambelkar et al. 2019). AGB stars are one of the major sites in galaxies that produce metals. Metals can be carried away from the AGB stars by radiation-driven AGB winds when dust forms. The speed of the AGB wind varies from 4 to 20 km s−1 (Höfner & Olofsson 2018), and a companion star may capture the wind with its gravity. In the case that there is an MS star close to an AGB star, a substantial fraction of the mass loss may be accreted onto the MS companion (Chen et al. 2017; Saladino et al. 2018, 2019). As a result, the metallicity of the companion may change. Such early stage low-mass stars become chemically peculiar, and their future evolution will be strongly affected. Carbon-enhanced-metal-poor (CEMP) stars (Beers & Christlieb 2005; Abate et al. 2013, 2015), Barium stars (Bidelman & Keenan 1951; Escorza et al. 2019), CH stars (Keenan 1942; McClure & Woodsworth 1990), and dwarf carbon stars (Dahn et al. 1977; Roulston et al. 2019) are common examples of the chemically peculiar stars. Their existence could be evidence of mass transfer during the previous AGB binary phase. The binarity of CH stars and CEMP stars has been studied (McClure & Woodsworth 1990; Starkenburg et al. 2014; Jorissen et al. 2016), confirming that many of them have companions. A number of recent studies show that the eccentricity of some of the aforementioned chemically peculiar stars may be large (Hansen et al. 2016; Jorissen et al. 2016, 2019; Van der Swaelmen 2017; Oomen et al. 2018), and their orbital periods range from hundreds to thousands of days. The nonzero eccentricity in these close binary stars indicates that some intense interactions that can pump the eccentricity may happen during their AGB binary phases. A strong correlation between a circumstellar disk and binarity has also been established in Galactic RV Tauri stars (Manick et al. 2017). Furthermore, many RV Tauri stars show a lack of refractory elements, which is called “depletion” (Giridhar et al. 1994; Van Winckel et al. 1998). Some researches suggest that the reaccretion of gas from a circumstellar disk around the post-AGB star (Gezer et al. 2019; Oomen et al. 2019) may induce the “depletion.” Besides the “smoking gun” evidence, observations also reveal that dusty circumbinary disks exist in binary systems with evolved stars (Kervella et al. 2015; Hillen et al. 2016; Homan et al. 2017; Ertel et al. 2019). The UV excess of some AGB stars also suggests that there could be accreting MS companions near them (Sahai et al. 2008; Ortiz & Guerrero 2016).

2021AandA...647A.140C__Gianninas_et_al._2016_Instance_1

In recent years, numerous low-mass and ELM WDs have been detected in the context of relevant surveys, such as the SDSS, ELM, SPY and WASP (see, e.g., Koester et al. 2009; Brown et al. 2010, 2016, 2020; Kilic et al. 2011, 2012; Gianninas et al. 2015; Kosakowski et al. 2020). The discovery of their probable precursors, namely, the so-called low-mass pre-WDs, has triggered an interest in these types of objects because of the possibility of studying the evolution of the progenitors that lead to the WD phase. Even more interestingly, the detection of multi-periodic brightness variations in low-mass WDs (Hermes et al. 2012, 2013a,b; Kilic et al. 2015, 2018; Bell et al. 2017, 2018; Pelisoli et al. 2018), and low-mass pre-WDs (Maxted et al. 2013, 2014; Gianninas et al. 2016; Wang et al. 2020) has brought about new classes of pulsating stars known as ELMVs and pre-ELMVs, respectively (ELM and pre-ELM variables, respectively). It has allowed for the study of their stellar interiors using the tools of asteroseismology, similarly to the case of other pulsating WDs such as ZZ Ceti stars or DAVs –pulsating WDs with H-rich atmospheres – and V777 Her or DBVs – pulsating WDs with He-rich atmospheres (Winget & Kepler 2008; Fontaine & Brassard 2008; Althaus et al. 2010; Córsico et al. 2019). The pulsations observed in ELMVs are compatible with global gravity (g)-mode pulsations. In the case of pulsating ELM WDs, the pulsations have large amplitudes mainly at the core regions (Steinfadt et al. 2010; Córsico et al. 2012; Córsico & Althaus 2014a), allowing for the study of their core chemical structure. According to nonadiabatic computations (Córsico et al. 2012; Van Grootel et al. 2013; Córsico & Althaus 2016), these modes are probably excited by the κ − γ (Unno et al. 1989) mechanism acting at the H-ionization zone. In the case of pre-ELMVs, the nonadiabatic stability computations for radial (Jeffery & Saio 2013) and nonradial p- and g-mode pulsations (Córsico et al. 2016; Gianninas et al. 2016; Istrate et al. 2016b) revealed that the excitation is probably due to the κ − γ mechanism, acting mainly in the zone of the second partial ionization of He, with a weaker contribution from the region of the first partial ionization of He and the partial ionization of H. The presence of He in the driving zone is crucial to having the modes destabilized by the κ − γ mechanism (Córsico & Althaus 2016; Istrate et al. 2016b).

2020ApJ...892L...3A___2019e_Instance_1

Because of its large mass, the discovery of GW190425 suggests that gravitational-wave analyses can access densities several times above nuclear saturation (see, e.g., Figure 4 in Douchin & Haensel 2001) and probe possible phase transitions inside the core of a neutron star (NS) (Oertel et al. 2017; Essick et al. 2019; Tews et al. 2019). However, binaries comprised of more massive stars are described, for a fixed EoS, by smaller values of the leading-order tidal contribution to the gravitational-wave phasing (Flanagan & Hinderer 2008). These are intrinsically more difficult to measure. For GW190425, this is exacerbated by the fairly low S/N of the event compared to GW170817. Overall, we find that constraints on tides, radius, possible p–g instabilities (Venumadhav et al. 2013; Weinberg et al. 2013; Weinberg 2016; Zhou & Zhang 2017), and the EoS from GW190425 are consistent with those obtained from GW170817 (Abbott et al. 2017b, 2019e). However, GW190425 is less constraining of NS properties, limiting the radius to only below 15 km, to below 1100 and only ruling out phenomenological p–g amplitudes above 1.3 times the 90% upper limit obtained from GW170817 at the same confidence level. The p–g constraints were obtained with a different high-spin prior than the rest of the results (see Appendix F.5) but the difference does not significantly change our conclusions. Spin priors can affect the inference of tidal and EoS parameters, and we note that the low-spin results are generally more constrained. Following Agathos et al. (2020), we estimate the probability of the binary promptly collapsing into a black hole (BH) after merger to be 96%, with the low-spin prior, or 97% with the high-spin prior. Repeating the analyses of Chatziioannou et al. (2017) and Abbott et al. (2019d), we find no evidence of a postmerger signal in the 1 s of data surrounding the time of coalescence. We obtain 90% credible upper limits on the strain amplitude spectral density and the energy spectral density of and , respectively, for a frequency of 2.5 kHz. Similar to GW170817, this upper limit is higher than any expected post-merger emission from the binary (Abbott et al. 2019d). More details on all calculations and additional analyses are provided in Appendix F.7.

2016ApJ...821..107G__Schwadron_et_al._2011_Instance_3

We repeated the plasma pressure calculation presented by Schwadron et al. (2011) and Fuselier et al. (2012) for the new ENA energy spectrum. The results for the downwind hemisphere and for the Voyager 1 region are summarized in Table 3. The measured intensity j ENA of neutralized hydrogen at a given energy translates into a pressure of the parent ion population in the heliosheath times the integration length along the line of sight, ΔP × l, in the following way: 3 Δ P × l = 4 π 3 n H m H v j ENA ( E ) σ ( E ) Δ E c f 4 c f = ( v + u R ) 2 v 4 ( v 2 + 4 u R 2 + 2 u R v ) . In Equation (3), ΔE denotes the width of the respective energy bin; for the typical radial velocity of solar wind in the flanks and the downwind hemisphere of the inner heliosheath, we assumed uR = 140 km s−1 as measured by Voyager 2, whereas uR = 40 km s−1 for the heliosheath in the Voyager 1 direction (Schwadron et al. 2011; Gloeckler & Fisk 2015). For the density of neutral hydrogen in the inner heliosheath a constant nH = 0.1 cm−3 is assumed (Schwadron et al. 2011; Gloeckler & Fisk 2015). The charge-exchange cross section between protons and neutral hydrogen decreases from (4 to 2) × 10−15 cm−2 for 0.015 to 1.821 keV (Lindsay & Stebbings 2005). The integration length l for ENA production in the plasma is approximately the thickness of the inner heliosheath. The part of Equation (3) without the velocity factor cf can be interpreted as stationary pressure. The total pressure or dynamic pressure is the stationary pressure times this factor. Integrating over all energy bins in Table 3, we obtain the total plasma pressure times heliosheath thickness as P × l = 304 pdyn cm−2 au for the downwind hemisphere and 66 pdyn cm−2 au for the Voyager 1 region (1 pdyn cm−2 au = 0.015 N m−1). If we want to put these numbers into the context of other studies, we face two problems. First, the uncertainty of the total pressure is large given the upper limits in the two lowest energy bins. Second, heliosheath plasma more energetic than 2 keV will produce ENAs that cannot be detected with IBEX-Lo. We therefore used the observed median j = 0 cm−2 sr−1 s−1 keV−1 for heliospheric ENAs in the two lowest energy bins of IBEX-Lo and relied on the study by Livadiotis et al. (2013). They compared the expected plasma pressure from a kappa distribution of protons with the plasma pressure derived from IBEX-Hi energy spectra: the energy range between 0.03 and 2 keV, roughly corresponding to the IBEX-Lo range, covered more than half of the total plasma pressure predicted from a kappa distribution. The authors found a total plasma pressure of P = 2.1 pdyn cm−2 for all sky directions except for the ENA Ribbon. Gloeckler & Fisk (2015) presented a multi-component plasma model for the heliosheath to explain Voyager and IBEX observations. At low energies they assumed the ENA energy spectra provided by Fuselier et al. (2012). They derived a total pressure of 2.5 pdyn cm−2 in all three plasma regions in the nose of the heliotail (Gloeckler & Fisk 2015). Pressure contributions from the slowed solar wind, magnetic pressure, and the pressure exerted from pickup ions and anomalous cosmic rays all had to be taken into account to obtain this total pressure.

2020MNRAS.497.3943M__Eckert_et_al._2012_Instance_1

The surface brightness profile for the mosaicked image of A2199 was then extracted in concentric annuli centred at the cluster centre (RA, Dec.) = (16:28:38.21, +39:33:02.31). This corresponds to the location of the peak X-ray flux in the cluster. The radial profile of the surface brightness is shown in the upper panel of Fig. 2. In the lower panel of this figure, we present the radial profile of the azimuthally averaged electron density of A2199. The profile is recovered from the deprojection of the median surface brightness profile using the onion peeling technique (Ettori et al. 2010), and assuming that the ICM plasma is spherical symmetry. To convert from surface brightness to density, we use the following widely used approach (Eckert et al. 2012; Tchernin et al. 2016; Ghirardini et al. 2018; Ghirardini et al. 2019; Walker et al. 2020). Using xspec and the response files for XMM–Newton, the conversion factor between APEC normalization and X-ray count rate in the 0.7–1.2 keV band is found. In the 0.7–1.2 keV band, this is largely independent of the temperature of the gas, so this allows a direct conversion from the deprojected X-ray surface brightness profile to a deprojected profile of APEC normalization. The APEC normalization is related to the gas density by the equation: (1)$$\begin{eqnarray*} {\rm Norm} = \frac{10^{-14}}{4 \pi [d_\mathrm{A}(1+z)]^2 }\int n_\mathrm{ e} n_\mathrm{H} \mathrm{d}V, \end{eqnarray*}$$where the electron and ion number densities are ne and nH, respectively (for a fully ionized plasma these are related by ne = 1.17nH, Asplund et al. 2009), the angular diameter distance to the cluster is dA, and z is the cluster redshift. The deprojected APEC normalizations are then converted to deprojected density in the usual fashion, assuming spherical symmetry and a constant density in each shell, by calculating the projected volumes, V, of each shell in the 2D annuli. When performing this conversion, we used a column density of 0.08 × 1022 cm−2 (from Kalberla et al. 2005), and using the abundance tables of Asplund et al. (2009).

2022MNRAS.514.2407S__Matsunaga_et_al._2011_Instance_1

The age distribution of the inner Galaxy is less well known. Traditionally, from photometry, the bulge has been viewed as an old structure (e.g. Zoccali et al. 2003) but this was thrown into question by spectroscopic ages of microlensed dwarfs (Bensby et al. 2013), many of which are young. Recent work by Bernard et al. (2018) constrained the age distribution of the bulge ($-5\, \mathrm{deg}\lesssim b\lt -2\, \mathrm{deg}$) from Hubble Space Telescope photometry of the main sequence turn-off stars, concluding that, although the bulge is predominantly old, approximately $10\, \mathrm{per\, cent}$ of stars are younger than $5\, \mathrm{Gyr}$. This fraction increases to $\sim 20\, \mathrm{per\, cent}$ for more metal-rich ($[\mathrm{Fe}/\mathrm{H}]\gtrsim 0.2\, \mathrm{dex}$) stars, consistent with the Bensby et al. (2013) work. Further evidence for a predominantly old ($\gtrsim 8\, \mathrm{Gyr}$) bulge comes (indirectly) from [C/N] measurements of giant stars (Bovy et al. 2019; Hasselquist et al. 2020), although, as highlighted by Hasselquist et al. (2020), age appears to correlate with both metallicity and Galactic height of the populations. Nogueras-Lara et al. (2020a) have used the luminosity of red clump stars to conclude the majority ($\sim 95\, \mathrm{per\, cent}$) of the nuclear stellar disc formed more than $8\, \mathrm{Gyr}$ ago with some evidence of a more recent ($\lt 1\, \mathrm{Gyr}$ ago) star formation burst (Matsunaga et al. 2011). This is consistent with ongoing/recent star formation within the central molecular zone (Morris & Serabyn 1996) and is broadly consistent with the conclusions of Bernard et al. (2018) on the wider bar/bulge but possibly suggesting the nuclear stellar disc is on average older than the surrounding bulge. We have fitted by-eye a very simple star formation history to the ‘cleanest’ combined fit from Bernard et al. (2018) of the form $\mathrm{sech}^2((13.5\, \mathrm{Gyr}-\tau)/4.7\, \mathrm{Gyr})$ with a truncation at $14\, \mathrm{Gyr}$. Combining the metallicity distributions and star formation histories with the PARSEC isochrones and adopting a Kroupa (2001) initial mass function, we have computed the luminosity function of the giant branch stars in the inner bulge region. We show the results in the lower panel of Fig. 4 along with a simple double Gaussian plus quadratic fit to represent the red clump stars, the red giant branch bump stars and the red giant branch stars respectively. We find that the lowest latitude bin has a red clump magnitude of $M_{K_s,\mathrm{RC}}=-1.61\, \mathrm{mag}$. This agrees well with the mean solar neighbourhood result from Chan & Bovy (2020) of $M_{K_s,\mathrm{RC}}=-1.622\, \mathrm{mag}$ and more specifically using their relations adopting the mean (J − Ks) = 0.647 (see Appendix B1) and mean metallicity $-0.18\, \mathrm{dex}$ gives $M_{K_s,\mathrm{RC}}=-1.595\, \mathrm{mag}$. The metallicity gradient with latitude produces a red clump magnitude gradient of $0.032\, \mathrm{mag}\, \mathrm{deg}^{-1}$ whilst using the change in mean metallicity in combination with the results of Chan & Bovy (2020) we would expect $0.024\, \mathrm{mag}\, \mathrm{deg}^{-1}$. At all latitudes the red clump distribution is well reproduced by a Gaussian with standard deviation $\sim 0.11\, \mathrm{mag}$. Chan & Bovy (2020) measured the solar neighbourhood red clump to have an intrinsic standard deviation of $0.097\, \mathrm{mag}$ which combined in quadrature with that arising from the metallicity variance predicts a standard deviation of $\sim 0.13\, \mathrm{mag}$, similar to the PARSEC models. The red clump peaks from the PARSEC isochrones have a slight bimodal structure arising from the bimodal metallicity distributions such that the mode typically peaks $\sim 0.03\, \mathrm{mag}$ fainter than the Gaussian mean.

2020MNRAS.494.5576P__Pastorello_et_al._2018_Instance_1

Another interesting type of transient to compare DES17X1boj and DES16E2bjy with are the SN impostors. As shown in Fig. 3, SN2009ip has a short phase of re-brightening around the same phase as the secondary peak of the DES-SN transients, and its peak brightness (MV = −17.7; see e.g. Fraser et al. 2013) is similar to DES16E2bjy. However, several other features distinguish our double-peaked DES-SN transients from the SN impostors. While SN2009ip does show re-brightening, its light-curve evolution is clearly different from the DES transients. Additionally, other impostor candidates such as SN2015bh (see e.g. Elias-Rosa et al. 2016) and SN2016bdu (Pastorello et al. 2018) have very similar light curves with SN2009ip, but do not exhibit rebrightening. Furthermore, our photometric data also constrain the long-term variability of DES17X1boj to a level below what was seen in SN2009ip (Pastorello et al. 2013) and SN2016bdu (Pastorello et al. 2018) in the years before the brightest event (MV in range −13 to −14). For the more distant event DES16E2bjy, such outbursts would have been below our detection threshold. Regarding the spectroscopic data, the impostors exhibit strong, narrow H and He lines around peak brightness (see e.g. Fraser et al. 2013; Mauerhan et al. 2013; Pastorello et al. 2013). No such features are seen in either of the DES transients (see Figs 6 and 9), but it is possible that the lines are hidden in the noise.To investigate this, we estimated the limiting equivalent width (EW) for a Gaussian-shaped narrow H α line with $v$FWHM = 500  km s−1 in our spectra. H lines with similar widths are often seen in both SN impostors (Smith et al. 2011) and in SNe IIn (Taddia et al. 2013) where $v$FWHM ∼ 100−1000  km s−1 are typically measured. For the given configuration, we found limits of EW ≲ 5 Å for DES17X1boj and EW ≲ 14 Å for DES16E2bjy. In the case of SNe IIn, the line strengths are typically measured in several tens to hundreds of Ångstroms (EW≳ 40 Å; Smith, Mauerhan & Prieto 2014), and thus it is unlikely that narrow H α lines are hiding in the spectra. Due to both photometric and spectroscopic differences, it is unlikely that DES17X1boj and DES16E2bjy are events similar to SN impostors.

2016ApJ...833....7Y__Owen_&_Wu_2013_Instance_2

We use the N-body simulation package—MERCURY (Chambers 1999)—to numerically investigate the effects of photo-evaporation on the dynamical evolution of planet–satellite systems. We choose the Bulirsch–Stoer integration algorithm, which can handle close encounter accurately. It is important in the simulations, as we will see below, that many close encounters among moons and the planet are expected to happen. Collisions among moons, the planet, and the central star are also considered in simulations and treated simply as inelastic collisions without fragmentations. Each simulation consists of a central star, a planet, and some moons orbiting around the planet. The photo-evaporation is simply modeled as a slow (adiabatic) and isotropic mass-loss process of the planet. In reality, the photo-evaporation is a very slow process on a timescale of the order of 107–108 year (Owen & Wu 2013). However, it is impractical and unnecessary to perform a simulation on such a long timescale. Instead, we model the mass-loss process on a timescale of τevap, and each simulation typically lasts for several τevap. As long as the adiabatic requirement is met, i.e., the mass-loss timescale is much longer than the dynamical timescale of the system (τevap ≫ Pp, where Pp is the orbital period of the planet), one could study the dynamical effects of the mass-loss process equivalently. As we discussed in Section 3.3, the results converge if τevap > 102–103 Pp, indicating the adiabatic condition is met. Therefore, in all other simulations, we set τevap = 104 Pp. Other parameters are set to represent the typical values of Kepler planets. In particular, we consider a planet–satellite system orbiting a star of solar mass (M⋆ = M⊙) in a circular orbit (ep = 0.0) with semimajor axis of ap = 0.1 au. The orbit has a period of ∼10 days (typical value of Kepler planets), and it is sufficiently close to the central star to be subject to significant photo-evaporation effect (Owen & Wu 2013), which removes massive hydrogen envelopes of the planet. The planet has an initial mass of Mpi and a final mass of Mpf after photo-evaporation. In this paper, we adopt Mpi = 20 M⊕ and Mpf = 10 M⊕ nominally (close to the standard model adopted in Owen & Wu 2013). The mean density of the planet is set to the same as that of Neptune (1.66 g cm−3). The effect of changing the planetary density is discussed in Section 3.3. We performed a number of sets of simulations by considering different planet–satellite configurations. Similar to the definition in MERCURY, hereafter, we define “small moons” as test particles (TPs) whose mutual gravity and corresponding effects on the planet and the star are ignored, while “big moons” are gravitationally important enough that their gravitational effects are fully considered. Table 1 lists the initial setups and parameters of various simulations, whose results are presented in the following subsections.

2019MNRAS.485.3715B__Reid_et_al._2014_Instance_1

The presumed WD cooling age is ≳5 Gyr. If it has a low mass, this age limit can be larger since after the Roche lobe detachment a proto-WD goes through the contraction phase until it reaches its cooling track (Istrate et al. 2014, 2016). The duration of this phase increases as the mass of the proto-WD decreases, and may last as long as ∼2 Gyr. The J0740 characteristic age of 3.75 Gyr (Table 1) is smaller than the WD age estimate. However, the observed pulsar period derivative and consequently its characteristic age can be biased by kinematic effects, i.e. the effects of the pulsar proper motion (Shklovskii effect; Shklovskii 1970), the acceleration towards the Galactic plane and the acceleration due to differential Galactic rotation (Nice & Taylor 1995). Using the J0740 proper motion value from Table 1, the Sun’s Galactocentric velocity and the distance (240 km s−1 and 8.34 kpc, respectively; Reid et al. 2014), we calculated these corrections to the pulsar period derivative: $\dot{P}_{\rm S}=3.0\times 10^{-21}$, $\dot{P}_{\rm G,\perp }=-1.6\times 10^{-22}$, $\dot{P}_{\rm G,p}=3.8\times 10^{-23}$ for the minimum and $\dot{P}_{\rm S}=6.9\times 10^{-21}$, $\dot{P}_{\rm G,\perp }=-2.2\times 10^{-22}$, $\dot{P}_{\rm G,p}=8.9\times 10^{-23}$ for the maximum pulsar distance estimates. The corresponding intrinsic characteristic ages are τi ∼ 5 and ∼8.5 Gyr, which are compatible with the cooling age.8 Thus, the considered binary system indeed can be very old and the presumed WD can be ultracool. This is not a unique situation. There are other examples of the objects with similar characteristics. One of them is the WD companion of PSR J0751+1807 (Bassa et al. 2006). Its colours (see Figs 2 and 3) indicate that the WD has a pure helium or mixed H/He atmosphere with a temperature T ∼ 3500–4300 K. Other examples are isolated ultracool white dwarfs WD J1102 (Hall et al. 2008; Kilic et al. 2012) and WD 0346+246 (Oppenheimer et al. 2001). These WDs have temperatures of about 3650 and 3300 K, respectively, and are best explained by the mixed atmosphere models (Gianninas et al. 2015).

2021MNRAS.507.2012B__Vogelsberger_et_al._2014a_Instance_1

Our simulations were run using the AREPO (Springel 2010; Pakmor, Bauer & Springel 2011; Pakmor et al. 2016; Weinberger, Springel & Pakmor 2020) moving-mesh magnetohydrodynamics (MHD) code. The code solves for gravity coupled with MHD. The gravity solver uses the PM-tree method (Barnes & Hut 1986) and the MHD solver uses a non-static unstructured grid formed by performing a Voronoi tesselation of the domain. AREPO has been used to produce simulations of the Universe at a wide range of scales. At the largest scales, we have uniform volume cosmological simulations such as the Illustris (Genel et al. 2014; Vogelsberger et al. 2014a; Nelson et al. 2015; Sijacki et al. 2015) and Illustris-TNG (Marinacci et al. 2018; Naiman et al. 2018; Nelson et al. 2018; Pillepich et al. 2018b; Springel et al. 2018; Nelson et al. 2019a,b; Pillepich et al. 2019) suites. These simulations have box sizes ranging from ∼50 to ∼300 Mpc and baryonic mass resolutions ranging from ∼105 to 107 M⊙. They have been largely successful in producing galaxy and SMBH populations consistent with observations, in Illustris (Vogelsberger et al. 2014b; Sales et al. 2015; Sijacki et al. 2015) and in TNG (Genel et al. 2018; Weinberger et al. 2018; Pillepich et al. 2018b; Donnari et al. 2019; Rodriguez-Gomez et al. 2019; Torrey et al. 2019; Habouzit et al. 2021; Übler et al. 2021). At the smallest scales, we have cosmological zoom simulation suites such as AURIGA (Grand et al. 2017) for individual milky-type galaxies, and HESTIA (High-resolutions Environmental Simulations of The Immediate Area) (Libeskind et al. 2020) for the Local Group. These simulations have been successful in reproducing observational results for the internal structures of galaxies (Blázquez-Calero et al. 2020; Cautun et al. 2020; Grand et al. 2020). All these developments make AREPO an ideal tool for the development of black hole models, which require a reliable modelling of the necessary physics over a large dynamic range.

2021MNRAS.504..228S__Lyne_&_Manchester_1988_Instance_1

In addition, broad-band polarization observations provide essential information about the pulsar radio emission mechanism, beam geometry, and the Galactic magneto-ionic ISM. Pulsars are among the most highly polarized radio sources known (e.g. Lyne & Smith 1968; Gould & Lyne 1998), and the polarization varies with observing frequency (e.g. Manchester, Taylor & Huguenin 1973; Johnston et al. 2008; Dai et al. 2015), providing insight into the magnetospheric emission and propagation mechanisms. In addition, the linear polarization P.A.s across pulse phase can constrain the beam size and inclination angles, with respect to the pulsar’s rotation axis and our line of sight (LoS). For example, the rotating vector model (RVM) predicts a smooth ‘S’-shape, due to the projected vectors of the magnetic field lines as they sweep across our LoS (e.g. Radhakrishnan & Cooke 1969; Lyne & Manchester 1988; Johnston et al. 2007). Many pulsars show more complex P.A. curves, particularly discontinuities with rapid jumps of ≈90°, which suggests the presence of two orthogonal polarization modes (OPM; e.g. Manchester 1975; Backer, Rankin & Campbell 1976). Furthermore, circular polarization across the pulse is observed to either remain in the same hand or change sense (e.g. Radhakrishnan & Rankin 1990), which may be intrinsic to the emission mechanism or due to propagation effects (e.g. Han et al. 1998; Kennett & Melrose 1998). Additional diagnostics of magnetospheric effects have also been investigated, including variations in Faraday rotation measure (RM) and circular polarization across the pulse (e.g. Ramachandran et al. 2004; Karastergiou 2009; Noutsos et al. 2009; Ilie, Johnston & Weltevrede 2019). Although pulsars were discovered over 50 yr ago (Hewish et al. 1968), it is clear that current understanding and models of the emission mechanism are far from replicating this wide range of observed behaviour, as well as additional emission phenomena such as nulling and mode changing.

2018MNRAS.476.3631P__Shi_&_Sheth_2018_Instance_1

The left-hand panel of Fig. 3 shows the scatter plot of b1 and mass, coloured by $1+\delta _{5\,h^{-1}\,{\rm Mpc}}$. There is an obvious correlation visible, with a largely vertical trend in which b1 increases monotonically with $\delta _{5\,h^{-1}\,{\rm Mpc}}$. The symbols with errors show the median bias as a function of mass, in four bins of $\delta _{5\,h^{-1}\,{\rm Mpc}}$ and averaged over 10 realizations of the default box. It is clear that, at fixed $\delta _{5\,h^{-1}\,{\rm Mpc}}$, the trend of bias with halo mass is weak. This trend is consistent with previous results in the literature, which have shown that large-scale bias is more strongly correlated with halo-centric overdensity than it is with halo mass (see e.g. Abbas & Sheth 2007; Shi & Sheth 2018). The right-hand panel of the figure explores this further, showing the Spearman rank correlation coefficient between b1 and δR as a function of halo mass, for R = 2, 3, 5 h−1 Mpc. We see that the strength of the correlation is only a weak function of mass for each smoothing scale, but monotically increases with R. This increase with R is not surprising, since our estimator for b1 itself is ultimately measuring a large-scale halo-centric overdensity, so that b1 and δR are measuring essentially the same quantity for large R. To appreciate this point better, Fig. 4 shows a visualization of the haloes in a subvolume of our high-resolution box, with haloes shown as circles whose radii scale with R200b and whose colour scales with halo bias b1 as indicated by the colour bar. The panels focus on massive (top) and low mass haloes (bottom). We discuss some connections between halo-by-halo bias and gravitational redshift measurements (see e.g. Wojtak, Hansen & Hjorth 2011; Croft 2013; Alam et al. 2017) in Appendix D. In Appendix B1, we present analytical arguments that explain the size of the scatter in b1 at fixed mass and also qualitatively reproduce the trends seen in Fig. 3.

2021MNRAS.500.3368S__Dubinski_1998_Instance_1

Like normal elliptical galaxies, the analysis of the stellar populations in the inner regions of BCGs indicates that the bulk of their stars was formed rapidly in a very intense starburst at redshift z > 2 (Renzini 2006). However, despite sharing similar morphologies with normal massive ellipticals, in addition to red colours, old and metal-rich stellar populations and alpha-enhancements (Brough et al. 2008; Loubser et al. 2009; Donahue et al. 2010; Loubser & Sánchez-Blázquez 2012; Barbosa et al. 2016; Edwards et al. 2020), BCGs constitute a special category of objects with peculiar star formation histories (SFHs) seen from both observations (Tran et al. 2008; Barbosa et al. 2016) and models (Dubinski 1998; De Lucia & Blaizot 2007). The evolution of BCGs is significantly affected by their surrounding environments. Due to their central positions in the gravitational potential well of their host clusters, central cluster galaxies accrete stars and gas from satellite galaxies that orbit around them and fall in, developing extended light profiles. The more representative SFHs of their dominant stellar population include components from in situ star formation, and from the interaction with other galaxies and with the intracluster medium, where the outer regions in BCGs are continually assembling mass through minor mergers (Cooke et al. 2019). The stellar populations of a large sample of observed BCGs have been recently studied in Edwards et al. (2020), from the galaxy core into the intracluster light (ICL) out to 4 effective radii, finding old stellar populations of ∼13 Gyr and high metallicities [Fe/H] ∼0.3 in the galaxy cores, whereas the average age in the ICL is estimated to be slightly younger, ∼9.2 Gyr with lower metallicities −0.4 [Fe/H] 0.2 at 40 kpc. This broadly supports the idea of two-phase galaxy formation, with the BCG cores and inner regions formed faster and earlier than the outer regions that have formed more recently, or have accreted mass afterwards through galaxy mergers and thereby also increasing in size (Oser et al. 2010; Kubo et al. 2017; Cooke et al. 2019).

2017AandA...606A..17M__Conselice_et_al._2003_Instance_1

Studies of SMGs over the past few tens of years have provided valuable insights into their properties. These include the redshift distribution (e.g. Chapman et al. 2005; Aretxaga et al. 2007; Wardlow et al. 2011; Yun et al. 2012; Smolčić et al. 2012; Simpson et al. 2014, 2017; Zavala et al. 2014; Miettinen et al. 2015a; Chen et al. 2016a; Strandet et al. 2016; Danielson et al. 2017; Brisbin et al. 2017), spatial clustering and environment (e.g. Ivison et al. 2000; Blain et al. 2004; Aravena et al. 2010; Hickox et al. 2012; Miller et al. 2015; Chen et al. 2016b; Wilkinson et al. 2017; Smolčić et al. 2017), merger incidence (e.g. Conselice et al. 2003), and circumgalactic medium (Fu et al. 2016). Regarding the intrinsic physical characteristics of SMGs, the properties studied so far include the sizes and morphologies (e.g. Swinbank et al. 2010; Menéndez-Delmestre et al. 2013; Aguirre et al. 2013; Targett et al. 2013; Chen et al. 2015; Simpson et al. 2015; Ikarashi et al. 2015; Miettinen et al. 2015b, 2017b,c; Hodge et al. 2016), panchromatic spectral energy distributions (SEDs; e.g. Michałowski et al. 2010; Magnelli et al. 2012; Swinbank et al. 2014; da Cunha et al. 2015; Miettinen et al. 2017a), stellar masses (e.g. Dye et al. 2008; Hainline et al. 2011; Michałowski et al. 2012; Targett et al. 2013), gas masses (e.g. Greve et al. 2005; Tacconi et al. 2006, 2008; Engel et al. 2010; Ivison et al. 2011; Riechers et al. 2011; Bothwell et al. 2013; Huynh et al. 2017), gas kinematics (e.g. Alaghband-Zadeh et al. 2012; Hodge et al. 2012; Carilli & Walter 2013; Olivares et al. 2016), and active galactic nucleus (AGN) incidence (Alexander et al. 2003, 2005; Laird et al. 2010; Johnson et al. 2013; Wang et al. 2013). The role played by SMGs in a broader context of galaxy formation and evolution has also been investigated through models (e.g. Baugh et al. 2005; Fontanot et al. 2007; Davé et al. 2010; González et al. 2011; Hayward et al. 2013) and observational approach (e.g. Swinbank et al. 2006; Toft et al. 2014; Simpson et al. 2014).

2021MNRAS.500.1817L__Abbott_et_al._2020b_Instance_2

Since the errors of the LIGO-estimated rates are dominated by Poisson statistics (Abbott et al. 2020a,b), we approximate the PDF for the expected number of detections $\mathcal {N}=\mathcal {R}VT$ (from the surveyed space–time volume VT) by $\mathrm{d}P/\mathrm{d}\mathcal {N}\propto \mathcal {N}^{k-1/2}\mathrm{e}^{-\mathcal {N}}/k!$, where k = 1 for each of the relevant cases ($\mathcal {R}_{190814}$, $\mathcal {R}_{170817}$, and $\mathcal {R}_{190425}$), and the factor of $\mathcal {N}^{-1/2}$ is from Jeffrey’s prior (Abbott et al. 2020a). From the median values of $\bar{\mathcal {R}}_{190814}=7\rm \, Gpc^{-3}\, yr^{-1}$ (Abbott et al. 2020a), $\bar{\mathcal {R}}_{\rm 170817}=760\rm \, Gpc^{-3}\, yr^{-1}$, and $\bar{\mathcal {R}}_{\rm 190425}=460\rm \, Gpc^{-3}\, yr^{-1}$ (Abbott et al. 2020b), we obtain the effective surveyed space–time volumes $VT=1.2/\bar{\mathcal {R}}$ for each of these three events (‘1.2’ is the median of $\mathrm{d}P/\mathrm{d}\mathcal {N}$). We consider both GW170817 and GW190425 as bNS mergers, because the component masses of GW190425 are not far from those of GW170817 and the nature of the merging objects makes little practical difference in our model. Thus, the PDF of the total bNS merger rate from the sum of the two is given by a convolution of the two individual PDFs (1)$$\begin{eqnarray*} {\mathrm{d}P\over \mathrm{d}\mathcal {R}_{\rm bns}} = \int _0^{\mathcal {R}_{\rm bns}} \mathrm{d}\mathcal {R}_1 {\mathrm{d}P\over \mathrm{d}\mathcal {R}_1} \left.{\mathrm{d}P\over \mathrm{d}\mathcal {R}_2}\right|_{\mathcal {R}_{\rm bns}-\mathcal {R}_1}, \end{eqnarray*}$$where we have written $\mathcal {R}_{1} = \mathcal {R}_{170817}$, $\mathcal {R}_{2} = \mathcal {R}_{190425}$ for brevity. We then calculate the PDF for the inverse of the total bNS merger rate $\mathrm{d}P/\mathrm{d}\mathcal {R}_{\rm bns}^{-1}=\mathcal {R}_{\rm bns}^2\mathrm{d}P/\mathrm{d}\mathcal {R}_{\rm bns}$. Finally, the PDF of the rate ratio $\beta =\mathcal {R}_{190814}/\mathcal {R}_{\rm bns}$ is given by (2)$$\begin{eqnarray*} {\mathrm{d}P\over \mathrm{d}\beta } = \int _0^\infty {\mathrm{d}\mathcal {R}_{3}\over \mathcal {R}_3} {\mathrm{d}P\over \mathrm{d}\mathcal {R}_{3}} \left.{\mathrm{d}P\over \mathrm{d}\mathcal {R}_{\rm bns}^{-1}}\right|_{\beta /\mathcal {R}_3}, \end{eqnarray*}$$where we have written $\mathcal {R}_{3} = \mathcal {R}_{190814}$ for brevity. We find the 90 per cent confidence interval for the rate ratio to be in the range $0.064\, \rm {per\, cent}\lt \beta \lt 2.8\, \rm {per\, cent}$.

2021MNRAS.504.1939G__Zhang_&_Yan_2011_Instance_1

For the magnetic field configurations considered in this work, the polarization angle can only change exactly by Δϕ = 90○ and a gradual change of the PA is not possible. There are tantalizing hints of a 90○ change in the PA in some of the GRBs, as discussed above, but the results are not yet conclusive. The result presented by Sharma et al. (2019) where the PA changes by 90○ twice over the emission is again very exciting as such a change over a single pulse can only occur for the Btor field configuration. The only difficulty, according to the modelling done here, is that both 90○ changes occur in the decaying tail of the pulse when high latitude emission dominates the flux. In the measurement presented by Sharma et al. (2019), the PA shows a change close to the peak of the emission. Another scenario in which a 90○ PA change can be obtained includes contribution from multiple pulses and when the LOS is close to the edge of the jet, such that θobs ≈ θj, along with a change in bulk Γ between the pulses which would change ξj = (Γθj)2. Alternatively, such a change in the PA can be obtained due to magnetic reconnection, e.g. in the ICMART model (Zhang & Yan 2011), where the local magnetic field orientation, which is orthogonal to the wave vector of the emitted photon, itself changes by 90○ as the field lines are destroyed and reconnected in the emission region (Deng et al. 2016). To obtain a change in the PA other than Δϕ = 90○ or to get a gradually changing PA the condition for axisymmetry must be relaxed and the magnetic field configuration or orientation in the emission region must change. One possibility is that if the different pulses that contribute to the emission arise in a ‘mini-jet’ within the outflow (e.g. Shaviv & Dar 1995; Lyutikov & Blandford 2003; Kumar & Narayan 2009; Lazar, Nakar & Piran 2009; Narayan & Kumar 2009; Zhang & Yan 2011). In this case, the different directions of the mini-jets or bright patches w.r.t. the LOS (e.g. Granot & Königl 2003; Nakar & Oren 2004) would cause the PA to also be different between the pulses even for a field that is locally symmetric w.r.t the local radial direction (e.g. B⊥ or B∥) as well as for fields that are axisymmetric w.r.t to the centre of each mini-jet (e.g. a local Btor for each mini-jet). Finally, broadly similar result would follow from an ordered field within each mini-jet (Bord) which are incoherent between different mini-jets. Time-resolved measurement in such a case would naturally yield a time-varying PA. Alternatively, as shown by Granot & Königl (2003) for GRB afterglow polarization, a combination of an ordered field component (e.g. Bord) and a random field, like B⊥, can give rise to a time-varying PA between different pulses that, e.g. arise from internal shocks. The ordered field component here would be that advected from the central engine and the random field component can be argued to be shock-generated. Notice that the ordered field component should not be axisymmetric in order for the PA to smoothly vary.

2015MNRAS.446.1799O__Fruscione_et_al._2006_Instance_1

A262 (RA = 01:52:46.299, Dec. = +36:09:11.80) is a bright, nearby poor cluster at z = 0.0162 (Struble & Rood 1999) with mean ICM temperature ≈2 keV( see e.g. Vikhlinin et al. 2005, 2006; Sato, Matsushita & Gastaldello 2009; Sanders et al. 2010). Due to its low mass and temperature, it may be considered as an intermediate between clusters and groups. A262 was observed for 110.7 ks in ACIS-S and a blank-sky observation of 450 ks was used for the background fitting. We used dmcopy tool in CIAO: Chandra's data analysis system (Fruscione et al. 2006) to restrict the energy range to 0.7−7 keV for both imaging and spectral analysis in all of the four data product files: event file, blank-sky observation, RMF and ARF. This reduced the number of PI channels to 433. Also, since the output of CIAO is in fits format, we used ftools-fv6 to export the event and background files as ASCII files. We then performed a 2 arcsec binning to both the event and the background files to generate the 3D data cubes for the spectral analysis without having to re-group the energy and PI columns. We used our in-house binning software tool for binning the data; this software is also available in the online package. This reduced the size of the data to a manageable level without adversely affecting the subsequent inference of the cluster. The output is a photon counts in a grid of 256 × 256 × 433 to be read in by bayes-x. Our Bayesian framework also allows us to analyse the data from one CCD. The X-ray images (Fig. 2) were then generated by summing up the counts at each pixel. To illustrate the large-scale features in the image we also binned the events with a cell size of 16 arcsec (right-hand panel of Fig. 2). It should be noted that the images are for illustration purposes only. We applied the rmfimg tool in CIAO to convert and expand RMF and ARF files into 2D images (matrices) for the spectral analysis. Similarly, we used ftools-fv to export the 2D RMF and ARF as ASCII files to be read in by bayes-x.

2015ApJ...808..157M__Nayfeh_1981_Instance_1

As we have seen, the asymptotic reduction of the original CR propagation problem, given by Equation (9), to its isotropic part cannot proceed to higher orders of approximation using a simple asymptotic series in Equation (10) and requires a multi-time asymptotic expansion. In the Chapman–Enskog method, the operator is expanded instead. Its purpose is to avoid unwanted higher time derivatives to appear in higher orders of approximation. This is very similar to, e.g., a secular growth in perturbed oscillations of dynamical systems. To eliminate the secular terms, one seeks to alter (also expand in a small parameter) the frequency of the zero-order motion, which is similar to the expansion. One example of such an approach may be found in a derivation of hydrodynamic equations for strongly collisional, but magnetized plasmas, starting from the Boltzmann equation (Mikhailovsky 1967). The classical monograph by Chapman & Cowling (1991; Ch.VIII) gives another example of a subdivision of the operator for solving the transport problem in a non-uniform gas-mixture. Expanding operators eliminates secular terms, such as the telegraph term. Perhaps more customary today, and equivalently, is to introduce a hierarchy of formally independent time variables (e.g., Nayfeh 1981) , so that 14 Instead of Equation (13), from Equation (9), we have 15 where the conditions are implied. The solution of this equation should be sought in the following form 16 where and are chosen to satisfy, respectively, the following two equations. 17 and 18 The solution for is as follows 19 and it can be evaluated for arbitrary n by expanding both sides of Equation (17) in a series of eigenfunctions of the diffusion operator on its lhs: For D = 1, for example, are the Legendre polynomials with , . The time dependent coefficients are determined by the initial values of (the anisotropic part of the initial CR distribution) and the rhs of Equation (17), that depends on , obtained at the preceding step. It is seen, however, that exponentially decay in time for and we may ignore them3 3 In fact, we must do so because our asymptotic method has a power accuracy in , but not the exponential accuracy. because we are primarily interested in evolving the system over time and even longer. Starting from n = 0 and using Equation (15), for the slowly varying part of f, we have 20 The solubility condition for (obtained by integrating both sides of Equation (15) in μ) also gives a trivial result 21 so the last two conditions are consistent with the suggested decomposition in Equation (16), since from Equation (18) with n = 1, we have 22 and, thus both and are, indeed, independent of and . We have introduced the function here by the following two relations 23 The solubility condition for yields the nontrivial and well-known (e.g., Jokipii 1966) result, which is actually the leading term of the expansion in 24 where The solubility conditions for , will generate the higher order terms of our expansion, which, after some algebra, can be manipulated into the following expressions for the third and fourth orders of approximation 25 26 We have denoted and . The pitch-angle diffusion coefficient and magnetic focusing σ are considered z-independent for simplicity, a limitation that can be easily relaxed by rearranging the operators containing in Equation (26). We can proceed to higher orders of approximation ad infinitum since terms containing can be expressed through . According to Equations (20)–(21), of interest is the evolution of on the timescales or ; thus, as we already mentioned, the contributions of to all of the solubility conditions, similar to those given by Equations (24)–(26), have to be dropped (because they become exponentially small) and only -contributions should be retained. Using Equations (20)–(21) and (24)–(26) to form the combinations and summing up both sides, on the lhs of the resulting equation, we simply obtain (see Equation (14)). Therefore, the evolution of up to the fourth order in ϵ takes the following form. 27 where , , and 28

2016ApJ...826..137M__Jewitt_et_al._2013_Instance_1

We performed a preliminary, zeroth-order analysis of the images by constructing a syndyne–synchrone map for each observing date. From those maps, we inferred that the activation time of the asteroid should be close in time to the discovery date, owing to the absence of dust features that could have shown up at the corresponding locations of synchrones approximately two months before discovery or earlier. In particular, no neck-line or trail features appear in the image from 2016 January 7 (PlAng ∼ 0°), which could have indicated past activity. In addition, there are no dust condensations along the direction of isolated synchrones, which could have indicated short bursts of activity (e.g., the case of P/2012 F5 (Gibbs), Moreno et al. 2012a), or several separated short bursts, as in the case of P/2013 P5 (Jewitt et al. 2013; Moreno et al. 2014). According to this, it is reasonable to start the search for a minimum in the function χ defined above by placing the activation time (t0) between a few days before the discovery date (102.5 days before perihelion) and about 60 days before. Regarding the duration of the activity, the smooth variation in absolute magnitudes (from Hv = 17.88 to Hv = 18.16, see Equation (1)) over the ∼40 days period of observation and the aforementioned lack of single-synchrone dust features would suggest a long-lasting process and not an impulsive, short-duration event, such as a collision with another body. In any case, we considered both long- and short-duration events by varying HWHM in a wide range between a few days and several months in the starting simplex of the search of the five-dimensional parameter space. For the peak dust mass loss, we imposed a wide range between a minimum of 0.1 kg s−1 and 100 kg s−1, while for the velocities we set broad limits for the parameters v0, v1, and v2, so that the velocities ranged from 0 to 5 × 103 m s−1 (the mean velocity in the asteroid belt), and the parameter γ from 0.5 to 0, i.e., from typical gas drag to a nearly flat distribution of velocities.

2017MNRAS.472.1152R__Cenko_et_al._2010_Instance_2

Alternatively, if a magnetar is the central engine powering GRBs, we might expect to see periodic features in the emission. Known magnetars have clear periodic signals in their emission caused by their rotation periods (e.g. Mazets et al. 1979; Kouveliotou et al. 1998). The X-ray pulsations typically contribute to 30 per cent of the signal, with a range of 10–80 per cent (Israel et al. 1999; Kargaltsev et al. 2012; Kaspi & Beloborodov 2017). There is an energy dependence on the pulsed fraction of the signal, where low energies tend to have smaller pulsed fractions (Vogel et al. 2014). Detection of a periodic signal during the plateau phase in the X-ray light curve would provide excellent supporting evidence for the magnetar central engine model. There have been searches for a periodic signal in the prompt emission of GRBs with a number of instruments with no success, for example: Burst And Transient Source Experiment (BATSE) GRBs ( Deng & Schaefer 1997), INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL) GRBs (Ryde et al. 2003), GRB 051103 (an extragalactic Soft Gamma-ray Repeater giant flare candidate detected by the Inter Planetary Network; Hurley et al. 2010) and Burst Alert Telescope (BAT) GRBs (Cenko et al. 2010; de Luca et al. 2010; Guidorzi et al. 2012). Dichiara et al. (2013) searched the prompt emission of a number of short GRBs for evidence of a precessing jet (predicted by Stone, Loeb & Berger 2013). However, these searches typically target the prompt emission and have not probed the regime where we might expect periodic signals from a magnetar central engine (i.e. during the plateau phase). Only two GRBs have been searched for periodic emission during the X-ray observations when the magnetar central engine may dominate the emission, GRB 060218 (Mirabal & Gotthelf 2010) and GRB 090709A (Mirabal & Gotthelf 2009; de Luca et al. 2010). The prompt emission of GRB 090709A possibly showed evidence of a periodic signal (Golenetskii et al. 2009; Gotz et al. 2009; Markwardt et al. 2009; Ohno et al. 2009), however this was ruled out with a more careful analysis of the prompt data from BAT, X-ray Telescope (XRT) and X-ray Multi-mirror Mission (XMM) observations of the X-ray afterglow (Cenko et al. 2010; de Luca et al. 2010). However, in the majority of these studies, the authors have targeted a constant spin period whereas a magnetar central engine is expected to have a rapidly decelerating spin period which would be very difficult to detect in standard searches for periodic signals. Dichiara et al. (2013) did conduct a deceleration search, however they were targeting signals in the prompt emission where we do not expect the signal from a spinning down magnetar.

2020ApJ...898L..33P__Delrez_et_al._2018_Instance_1

For the TRAPPIST-1 system, data obtained by HST provide initial constraints on the extent and composition of the planet’s atmospheres, suggesting that the four innermost planets do not have a cloud/haze-free H2-dominated atmosphere (de Wit et al. 2016, 2018). However, follow-up work by Moran et al. (2018) have shown that HST data can also be fit to a cloudy/hazy H2-dominated atmosphere. Complementary to HST, NASA’s Spitzer Space Telescope—which played a major role in the discovery and orbital determination of TRAPPIST-1d, e, f, and g (Gillon et al. 2017)—has also allowed us to put additional constraints on the atmospheric composition of TRAPPIST-1b. Transit observations with Spitzer (Delrez et al. 2018) have found a +208 ± 110 ppm difference between the 3.6 and 4.2 μm bands, suggesting CO2 absorption. Spitzer also showed that transit depth measurements do not show any hint of significant stellar contamination in the 4.5 μm spectral range. Morris et al. (2018) reached the same conclusion using a “self-contamination” approach based on the Spitzer data set. Spitzer's “Red Worlds” Program encompassed over 1000 hours of observations of the TRAPPIST-1 system, whose global results have been presented (Ducrot et al. 2020). HST and Spitzer measurements have also been combined with transit light curves obtained from space with K2 (Luger et al. 2017) and from the ground with the SPECULOOS-South Observatory (Burdanov et al. 2018; Gillon 2018) and Liverpool Telescope (Steele et al. 2004) where Ducrot et al. (2018) produced featureless transmission spectra for the planets in the 0.8–4.5 μm wavelength range, showing an absence of significant temporal variations of the transit depths in the visible. Additional ground-based observations with the United Kingdom Infra-Red Telescope, Anglo-Australian Telescope, and Very Large Telescope also show no substantial temporal variations of transit depths for TRAPPIST-1 b, c, e, and g (Burdanov et al. 2019). While the K2 optical data set detected a 3.3 day periodic 1% photometric modulation, it is not present in the Spitzer observations (Delrez et al. 2018). Further constraints on the molecular weight and presence/absence of atmospheres on the TRAPPIST-1 planets will require additional observations with future facilities.

2021ApJ...907...55M__Wang_2016_Instance_1

Elsässer variables (Elsässer 1950) are usually employed in solar wind studies to separate the outward-propagating waves (denoted by z+) and the reflected or inward propagating waves (denoted by z−). The separation is exact for even fully nonlinear, unidirectionally propagating waves in homogeneous and incompressible plasma, i.e., Alfvén waves, and it even holds for radially inhomogeneous (along a purely radial magnetic field) but otherwise homogeneous plasma without nonlinear interactions (Hollweg & Isenberg 2007; Magyar et al. 2019b). However, beyond pure Alfvén wave dynamics, it is often overlooked that transverse inhomogeneities, compressibility, and the nonlinear interaction of waves renders the separation of fluctuations into inward and outward-propagating waves inexact. For example, inhomogeneity and compressibility allows for waves (e.g., fast, slow MHD waves, surface Alfvén waves, kink waves, etc.) that are mostly described by both Elsässer variables as they propagate (Magyar et al. 2019a, 2019b). In fact, waves other than pure Alfvén waves generally perturb both Elsässer variables as they propagate. While kink waves, both propagating and standing, are routinely observed in the corona (e.g., Nakariakov et al. 1999; Tomczyk et al. 2007; Anfinogentov et al. 2015; Wang 2016; Nechaeva et al. 2019), evidence of surface Alfvén waves in the solar wind is as of yet inconclusive (e.g., Horbury et al. 2001; Vasquez et al. 2001; Paschmann et al. 2013). Besides waves that are not pure Alfvén waves, structures (inhomogeneities) advected by the solar wind also perturb both Elsässer variables (Tu & Marsch 1990, 1995; Zank et al. 2012; Adhikari et al. 2015). The nonlinear interaction of Alfvén waves can generate purely magnetic fluctuations, 2D modes (k∥ = 0) or condensates which as well perturb both Elsässer variables (Boldyrev & Perez 2009; Howes & Nielson 2013). Indeed, the nature of the inward z− Elsässer variable is often not clear (Wang et al. 2018). Previous studies on Alfvén wave dynamics in radially inhomogeneous models often mention the existence of an “anomalous” z− component that is co-propagating with z+ (Velli et al. 1989; Verdini et al. 2009; Perez & Chandran 2013). The issue of anomalous waves is solved by Hollweg & Isenberg (2007), who showed that, while the continuously generated, reflected z− components might show up as co-propagating in a harmonic analysis, their impulse response analysis shows that these reflected Alfvén waves still follow sunward characteristics, i.e., that there are no truly co-propagating Elsässer variables in these studies. Nevertheless, the coherence of the Elsässer variables resulting from this linear coupling of Alfvén waves seems to influence their spectrum (Verdini et al. 2009).

2020AandA...641A.139D__Dvorak_et_al._(2015)_Instance_2

The use of N-body simulations that include fragmentation allows us to perform a more detailed study of the final composition of the planets formed. In particular, we can study the water loss and/or accretion of the final planets more realistically than in the classic models of accretion. Marcus et al. (2010) presented two empirical models for the mantle stripping in differentiated planetary embryos after a collision. The authors set a simple planet structure of two layers, assuming differentiation in core and mantle, where the mantle could be composed by silicate or ice. In this work, the authors concluded that the more energetic the collision, the more mass from the mantle is lost. Therefore, for violent collisions, water could be more easily removed. Dvorak et al. (2015) performed SPH (smoothed particle hydrodynamics) simulations and studied water loss in planetary embryos and water retained in significant fragments after a collision. They concluded that the impact velocity and the impact angle play a key role in the water loss of a planetary embryo after a collision. The investigations developed by Marcus et al. (2010) and Dvorak et al. (2015) suggest that incorporating a realistic model of volatile transport and removal in an N-body code, may lead to reduced water contents on the resulting terrestrial-like planets, in comparison with those derived from classical models that assume perfect mergers. Burger et al. (2018) studied the volatile loss and transfer. The authors focused on hit-and-run encounters using SPH simulations. They concluded that the cumulative effect of several hit-and-run collisions could efficiently strip off volatile layers of protoplanets. Driven by this, Dugaro et al. (2019) studied the water delivery in planets formed in the habitable zone (HZ), using the mantle stripping models derived by Marcus et al. (2010) in their N-body simulationswith fragmentation. The authors showed that fragmentation is not a barrier for the surviving of water worlds in the HZ, and fragments may be important in the final water content of the potentially habitable terrestrial planets formed in situ.

2021ApJ...908...95H__Díaz-Sánchez_et_al._2017_Instance_1

Here we outline our sample of strongly lensed Planck-selected, dusty star-forming galaxies, hereafter “LPs” (Table 1). Our sample of 24 LPs began with a Planck and Herschel cross-match identification of eight objects (8/24) with continuum detections at 857 GHz (Harrington et al. 2016) greater than 100 mJy. The remaining 16/24 LPs were selected based on continuum detections by Planck, at 857, 545, and/or 353 GHz in the maps of all the available, clean extragalactic sky. These bright Planck point sources were then analyzed through a filtering process using a WISE color selection for the four WISE bands (3.4, 4.6, 12, 22 μm; Yun et al. 2008; D. Berman et al. 2021, in preparation). Other methods to identify strong gravitational lenses using (sub)millimeter data were independently verified by other teams using Planck and Herschel color criteria (Cañameras et al. 2015). The 24 LPs presented in these analyses include eight systems identified by Cañameras et al. (2015). The use of Planck and WISE data resulted in the discovery of the brightest known, dusty starburst galaxy at z > 1, the “Cosmic Eyebrow” (Díaz-Sánchez et al. 2017; Dannerbauer et al. 2019), which has also been independently recovered as one of the LPs presented in this survey work. Note that LPsJ1329 corresponds to the location on the sky associated with the Cosmic Eyebrow-A lens component (Dannerbauer et al. 2019). Table 1 shows the size of the lensed emission for each of the LPs, in which there are 21/24 with lens sizes ≤10″. Half of the LPs are galaxy–galaxy lenses, while the other half are a mix of cluster or group lensing. The foreground lens galaxies have a negligible contribution to the observed FIR emission of the lensed galaxy (Harrington et al. 2016). The LPs have CO-based spectroscopic redshifts ranging from zCO ∼ 1.1 to 3.6 (Harrington et al. 2016, 2018; Cañameras et al. 2018b; this work). They are comparable or brighter in CO and FIR luminosity than other strongly lensed SPT- (Weiß et al 2013; Strandet et al. 2016, 2017) or Herschel-selected; dusty star-forming galaxies (Harris et al. 2012; Bussmann et al. 2013, 2015; Yang et al. 2017). The Planck and Herschel wavelength selections preferentially target z ∼ 2–3 galaxies, versus the millimeter-selected SPT sources with a median closer to z ∼ 4, although with a wide range between z ∼ 2 and 7 (Weiß et al 2013; Spilker et al. 2016; Strandet et al. 2016; Reuter et al. 2020).

2016AandA...588A..42S__Hopkins_et_al._2010_Instance_1

Classical bulges (hereafter ClBs) are the central building blocks in many early-type spiral galaxies. Classical bulges might have formed as a result of major mergers during the early phase of cosmic evolution (Kauffmann et al. 1993; Baugh et al. 1996; Hopkins et al. 2009; Naab et al. 2014), or through a number of other mechanisms such as the monolithic collapse of primordial gas clouds (Eggen et al. 1962), the coalescence of giant clumps in gas-rich primordial galaxies (Noguchi 1999; Immeli et al. 2004; Elmegreen et al. 2008), violent disk instability at high-redshift (Ceverino et al. 2015), multiple minor mergers (Bournaud et al. 2007; Hopkins et al. 2010), and accretion of small companions or satellites (Aguerri et al. 2001). Although most of these studies do not provide quantitative predictions for the bulge kinematics, it is generally believed that ClBs formed through these processes have low rotation compared to the random motion. For example, Naab et al. (2014) showed that spheroids produced by minor and major mergers (which include ClBs) in full cosmological hydrodynamical simulations have a wide range of rotational properties; the massive ones have V/σ less than 0.5. Elmegreen et al. (2008) reported dispersion dominated clump-origin ClBs with upper limit on V/σ ~ 0.4−0.5, where V is the rotation velocity and σ is the central velocity dispersion. A similar study by Inoue & Saitoh (2012) suggests that clump-origin bulges have exponential surface density profiles and rotate rapidly with V/σ ~ 0.9, resembling pseudobulges (Kormendy & Kennicutt 2004). However, using cosmological hydrodynamical simulations with continuous gas accretion, Ceverino et al. (2015) showed that massive classical bulges with non-zero angular momenta are produced at high redshift, but provided no estimate on the bulge V/σ. Overall, there is no clear quantitative picture of the rotational motion induced during the formation of classical bulges in numerical simulations.

2021AandA...648A...5M__Windhorst_et_al._(1990)_Instance_2

Another important consistency check regards the angular size distribution of the sources. Figure 6 shows the cumulative size distributions of the final catalogs combined together, in four flux density bins (yellow solid lines). Such distributions can be considered reliable only down to a flux-dependent minimum intrinsic size (see vertical gray lines), below which most of the sources cannot be reliably deconvolved and they are conventionally assigned Θ = 0. The observed distributions are compared with various realizations of the cumulative distribution function described by Eq. (6), obtained by varying either the function exponent q (left and right columns respectively) or the assumed median size – flux relations (see various black lines).The original function proposed by Windhorst et al. (1990) (Eq. (6) with q = 0.62, see left column) does provide a good approximation of the observed distributions, when assuming the original Θmed − S relation described by Eq. (7), only at flux densities S150 MHz≳10 mJy (see long-dashed lines). This is perhaps not surprising considering that this relation was calibrated at 1.4 GHz down to a few mJy fluxes. At the lowest flux densities (S150 MHz≲1 mJy) we need to assume a steepening of the parameter m (see Eq. (8)), to get a good match with observations (dotted line in the top left panel). This is consistent with what proposed for higher frequency deep surveys (as discussed earlier in this section). At intermediate fluxes (S150 MHz ~ 1−10) mJy, on the other hand, none of the discussed median size – flux relations can reproduce the observed size distribution (see second-row panel on the left). It is interesting to note, however, that if we assume a steeper exponent for the distribution function described by Eq. (7) (i.e., q = 0.80), we get a very good match with observations at all fluxes, when assuming a flux-dependent scaling factor (k = k(S); see Eq. (9)) for the Windhorst et al. (1990) median size – flux relation (black solid lines on the right). The median sizes derived from the T-RECS simulated catalogs (Bonaldi et al. 2019) also provide good results for q = 0.80 (dot-dashed lines on the right), except again at intermediate fluxes (S150 MHz ~ 1−10), where they show strong discrepancies with observations also in Fig. 5. This seems to indicate that the number density of extended radio galaxies in this flux density range is over-estimated in the T-RECS simulated catalogs.

2017ApJ...849..123M__Ansdell_et_al._2016_Instance_1

Until recently, only β Pic and 49 Cet were known as CO-bearing debris disks (Vidal-Madjar et al. 1994; Zuckerman et al. 1995; Roberge et al. 2000). Later, more such objects were discovered, and the first statistical studies could be done (Greaves et al. 2016; Lieman-Sifry et al. 2016; Péricaud et al. 2017). Our present list partly incorporates these earlier samples and extends them to a full list of 17 dust-rich debris disks in the solar neighborhood (Section 2). In Figure 3(a), we display the 12CO 2–1 (or 12CO 3–2 for HR 4796) line luminosities (or upper limits), as a function of the fractional luminosity for our sample. Line luminosities of detected CO-bearing disks span almost two orders of magnitude, in which the brightest disks have luminosities that are comparable to those of fainter Herbig Ae and T Tauri disks (Ansdell et al. 2016; Péricaud et al. 2017). Since the sensitivity of the HR 4796 observation is nearly two orders of magnitude worse than that of the other measurements, we discard this object from the following analysis, reducing the size of our statistical sample to 16. For the other objects, by adopting the highest upper limit (source No. 7) in Figure 3(a), we derived a detectability threshold of ∼1.4 ×104 Jy km s−1 pc2 for the 12CO 2–1 line luminosity. With our 3 new discoveries, we found 11 disks in this sample that harbor CO gas, resulting in a very high detection rate of 68.8 − 13.1 + 8.9 % . Because of the small sample, we computed the uncertainties (corresponding to 68% confidence interval) using the binomial distribution approach proposed by Burgasser et al. (2003). Our result indicates that the presence of CO gas in dust-rich debris disks around young A-type stars is more likely the rule than the exception. The obtained incidence rate of 11/16 is valid above our detectability threshold for the 12CO J = 2–1 line luminosities. Nevertheless, we cannot rule out that all of our targets harbor CO gas at some level. Remarkably, as Figure 3(a) shows, apart from HR 4796, all of the disks with L disk / L bol > 2 × 10 − 3 contain detectable levels of CO gas.

2022MNRAS.516.3900A__Calmonte_et_al._2016_Instance_1

Sudden outbursts of NH3 simultaneously with H2S detected with the ROSINA-DFMS instrument on the Rosetta S/C point to the presence of abundant ammonium hydrosulphide in or on carbonaceous grains from comet 67P/Churyumov-Gerasimenko. There seems to be a clear distinction between the nucleus ice, where H2S and NH3 exist independently and grains, where they desorb together. S2 is much more abundant on grains compared to water than in the ice of the comet, while S3 is found only in grain impacts. This higher abundance points to radiolysis in these grains, which means they must have been exposed to energetic particles over an extended time. While for operational reasons, S4 could not be measured close to the dust impacts, S4 was clearly identified in periods where the coma was very dusty (Calmonte et al. 2016). Longer sulphur chains very likely are refractory, not sublimating at temperatures reached in the instrument or on grains in the coma. While Sn can also be formed from pure H2S ice by photo processing (Cazaux et al. 2022), the fact that S3 is clearly related to dust and is not found in the normal nucleus ice, where H2S is quite abundant, indicates that S3 is a product of radiolysis of the ammonium salt. In addition, photo processing of H2S results not only in Sn, but also in H2S2 (Cazaux et al. 2022), a species not detected in the DFMS m/z 66 and m/z 65 (HS2) spectra. This exposure rules out a contemporary formation of the salt on the surface or in the interior of the comet or a formation of the salt in the mid-plane of the protoplanetary disc, while the comet accreted. A pre-stellar formation is therefore likely. The salt is semivolatile, less volatile than water and could probably have survived quite high temperatures. It seems that on these grains, acids and ammonia are all locked in salts, be it sulphur, halogens, or carbon bearing acids like HOCN. If indeed, a relatively large part of sulphur and nitrogen is therefore in a semivolatile state in these grains, then the depletion of nitrogen in comets and of sulphur in star-forming regions could probably be explained, primarily because salts escape detection unless they experience temperatures above water sublimation. With the JWST S/C in orbit, there is hopefully the possibility to detect salts, or at least several of the acids in ices, which are supposed to be part of ammonium salt, like HOCN, H2CO, and formamide while looking for ammonium salts in star-forming regions and possibly comets.

2019MNRAS.482.3288G__Mayer_2013_Instance_1

The orbital decay of BSBHs may slow down or stall at ∼pc scales (e.g. Begelman et al. 1980; Milosavljević & Merritt 2001; Zier & Biermann 2001; Yu 2002; Vasiliev, Antonini & Merritt 2014; Dvorkin & Barausse 2017; Tamburello et al. 2017), or the barrier may be overcome in gaseous environments (e.g. Gould & Rix 2000; Escala et al. 2004; Hayasaki, Mineshige & Sudou 2007; Hayasaki 2009; Cuadra et al. 2009; Lodato et al. 2009; Chapon, Mayer & Teyssier 2013; Rafikov 2013; del Valle et al. 2015), in triaxial or axisymmetric galaxies (e.g. Yu 2002; Berczik et al. 2006; Preto et al. 2011; Khan et al. 2013, 2016; Vasiliev, Antonini & Merritt 2015; Gualandris et al. 2017; Kelley, Blecha & Hernquist 2017a), and/or by interacting with a third SMBH in hierarchical mergers (e.g. Valtonen 1996; Blaes, Lee & Socrates 2002; Hoffman & Loeb 2007; Kulkarni & Loeb 2012; Tanikawa & Umemura 2014; Bonetti et al. 2018). The accretion of gas and the dynamical evolution of BSBHs are likely to be coupled (Ivanov, Papaloizou & Polnarev 1999; Armitage & Natarajan 2002; Haiman, Kocsis & Menou 2009; Bode et al. 2010, 2012; Farris, Liu & Shapiro 2010, 2011; Kocsis, Haiman & Loeb 2012; Shi et al. 2012; D’Orazio, Haiman & MacFadyen 2013; Shapiro 2013; Farris et al. 2014, 2015) such that the occurrence rate of BSBHs depends on the initial conditions and gaseous environments at earlier phases (e.g. thermodynamics of the host galaxy interstellar medium; Dotti et al. 2007, 2009; Dotti, Sesana & Decarli 2012; Fiacconi et al. 2013; Mayer 2013; Tremmel et al. 2018). Quantifying the occurrence rate of BSBHs at various merger phases is therefore important for understanding the associated gas and stellar dynamical processes. This is a challenging problem for three main reasons. First, BSBHs are expected to be rare (e.g. Foreman, Volonteri & Dotti 2009; Volonteri, Miller & Dotti 2009), and only a fraction of them accrete enough gas to be ‘seen’. Secondly, the physical separations of BSBHs that are gravitationally bound to each other (≲a few pc) are too small for direct imaging. Even VLBI cannot resolve BSBHs except for in the local universe (Burke-Spolaor 2011). CSO 0402+379 (discovered by VLBI as a double flat-spectrum radio source separated by 7 pc) remains the only secure case known (Rodriguez et al. 2006; Bansal et al. 2017, see Kharb, Lal & Merritt 2017; however, for a possible 0.35-pc BSBH candidate in NGC 7674). Thirdly, various astrophysical processes complicate their identification such as bright hot spots in radio jets (e.g. Wrobel, Walker & Fu 2014b). Until recently, only a handful cases of dual active galactic nuclei (AGNs) – galactic-scale progenitors of BSBHs – were known (Owen et al. 1985; Junkkarinen et al. 2001; Komossa et al. 2003; Ballo et al. 2004; Hudson et al. 2006; Max, Canalizo & de Vries 2007; Bianchi et al. 2008; Guidetti et al. 2008). While great strides have been made in identifying dual AGNs at kpc scales (e.g. Gerke et al. 2007; Comerford et al. 2009, 2012, 2015; Green et al. 2010; Liu et al. 2010, 2013, 2018; Fabbiano et al. 2011; Fu et al. 2011, 2012, 2015a,b; Koss et al. 2011, 2012, 2016; Rosario et al. 2011; Teng et al. 2012; Woo et al. 2014; Wrobel, Comerford & Middelberg 2014a; McGurk et al. 2015; Müller-Sánchez et al. 2015; Shangguan et al. 2016; Ellison et al. 2017; Satyapal et al. 2017), there is no confirmed BSBH at sub-pc scales (for recent reviews, see e.g. Popović 2012; Burke-Spolaor 2013; Bogdanović 2015; Komossa & Zensus 2016).

2015ApJ...806....1M__Ahn_et_al._2012_Instance_1

For the clustering measurements, we use the sample of galaxies compiled in Data Release 11 (DR11) of the SDSS-III project. The SDSS-III is a spectroscopic investigation of galaxies and quasars selected from the imaging data obtained by the SDSS (York et al. 2000) I/II covering about 11,000 deg2 (Abazajian et al. 2009) using the dedicated 2.5 m SDSS Telescope (Gunn et al. 2006). The imaging employed a drift-scan mosaic CCD camera (Gunn et al. 1998) with five photometric bands ( and z; Fukugita et al. 1996; Smith et al. 2002; Doi et al. 2010). The SDSS-III (Eisenstein et al. 2011) BOSS project (Ahn et al. 2012; Dawson et al. 2013) obtained additional imaging data of about 3000 deg2 (Aihara et al. 2011). The imaging data was processed by a series of pipelines (Lupton et al. 2001; Pier et al. 2003; Padmanabhan et al. 2008) and corrected for Galactic extinction (Schlegel et al. 1998) to obtain a reliable photometric catalog. This catalog was used as an input to select targets for spectroscopy (Dawson et al. 2013) for conducting the BOSS survey (Ahn et al. 2012) with the SDSS spectrographs (Smee et al. 2013). Targets are assigned to tiles of diameter 3° using an adaptive tiling algorithm designed to maximize the number of targets that can be successfully observed (Blanton et al. 2003). The resulting data were processed by an automated pipeline which performs spectral classification, redshift determination, and various parameter measurements, e.g., the stellar-mass measurements from a number of different stellar population synthesis codes which utilize the photometry and redshifts of the individual galaxies (Bolton et al. 2012). In addition to the galaxies targeted by the BOSS project, we also use galaxies that pass the target selection but have already been observed as part of the SDSS-I/II project (legacy galaxies). These legacy galaxies are subsampled in each sector so that they obey the same completeness as that of the CMASS sample (Anderson et al. 2014).

2021AandA...650A.133J__Goss_&_Field_(1968)_Instance_1

Collisions with charged particles, namely with electrons and heavier ions are not considered in the radiative-transfer calculations presented in this paper, but may play an important role in the excitation of the CH Λ-doublet, particularlyin regions with high electron fractions, $x_{\textrm{e}} = n_{\textrm{e}}/(n_{\textrm{H}} 2n_{\textrm{H}_{2}}) >10^{-5}$xe=ne/(nH+2nH2)>10−5 –10−4. Such high electron fractions are ubiquitous in the diffuse molecular clouds present along the lines of sight studied here and may even be prevalent in the clouds surrounding the observed HII regions themselves, making electrons an important collision partner at low gas temperatures T ≤ 100 K. Bouloy & Omont (1977, 1979) studied the impact of collisional excitation by electrons on Λ-doublet transitions with particular emphasis on the ground-state Λ-doublet transitions of OH. These authors compute the collisional rate coefficients for collisions with electrons, either using perturbation methods such as those used by Goss & Field (1968) or using the Born approximation, both of which yield comparable results. These authors concluded that collisions with electrons, while incapable of inducing level inversion in the ground-state lines of OH at 18 cm, are responsible for thermalising them. Bouloy et al. (1984) further studied the excitation conditions of the ground-state lines of CH, and model the excitation by considering the radiative and collisional (de-) excitation of CH with H, H2, and electrons. Their results once again point to the role played by the collisions with electrons in thermalising the CH lines rather than inverting them. However, the excitation temperature of OH is found to be a 1–2 K above TCMB as derived from the resolved optical spectra of the OH A − X band transitions (Felenbok & Roueff 1996) or when measured by comparing the emission and absorption profiles of the radio L-band transitions of OH (Liszt & Lucas 1996, and references therein). This implies that densities much higher than the critical density are needed for thermalisation, which might also be the case for CH. More recently, Goldsmith & Kauffmann (2017) examined the impact of electron excitation on high-dipole-moment molecules like HCN, HCO+, CS, and CN in various interstellar environments. As long-range forces dominate the collisional cross-sections for electron excitation, the cross-sections and, in turn, the collisional rate coefficients scale with the square of the electric dipole moment, μe. Hence, the electron collisional rate coefficients for CH with μe = 1.46 D (Phelps & Dalby 1966) are ≃ 25 % of those of HCN with μe = 2.985 D (Ebenstein & Muenter 1984). Scaling the value of the HCN–e− collisional rate coefficient at T ≤ 100 K from Faure et al. (2007), we find the CH–e− collisional rates to be of the order of ~ 2 × 10−5 cm3 s−1. From this we can compute the critical electron fractional abundance, x* (e−), which defines the fractional abundance of electrons required for the collisional rate coefficients with electrons to be the same as that with H2 such that x* (e−) = ncrit(e−)∕ncrit(H2). Under the validity of these assumptions, x*(e−) for CH is ~10−6, making CH likely to be affected by electron excitation. Therefore, a complete treatment of the radiative–collisional (de-)excitation of the CH ground state would still require the availability of accurate collisional rate coefficients for collisional excitation by electrons. However, to our knowledge, these are currently not available.

2022MNRAS.516.5289M__Thompson_et_al._2015_Instance_3

Given the number densities within the mass-dissociation index plane of Fig. 8, we now ask ourselves whether known dissociated clusters, such as the Bullet cluster, are expected in L210N1024NR? The Bullet Cluster has a mass of $\sim 1.5 \times 10^{15} \, {\rm M}_{\odot }$ (e.g. Clowe et al. 2004; Bradač et al. 2006; Clowe et al. 2006) and we estimated a dissociation index of SBullet ∼ 0.335 ± 0.06. As seen in Fig. 8 there are no Bullet cluster analogues (structures of approximate mass and dissociation) in L210N1024NR, this is unsurprising as a simulation requires a significantly larger volume than that of L210N1024NR ((210cMpc h−1)3) to expect such an object (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015; Kraljic & Sarkar 2015; Thompson et al. 2015). From the distribution presented in Fig. 8, it is trivial to estimate the required cosmological volume (the effective volume, Veff) to expect structures of a given mass and dissociation index. By separating the 2D distribution on the mass-dissociation index planes into the component 1D distributions of mass and dissociation the effective volume is computed as (12)$$\begin{eqnarray} V_\text{eff}~^{-1} &=&\int \int \,{\rm{ d}} S \, {\rm{ d}} M \phi (S, M) \\ &=& \int _{S_\text{a}}^{S_\text{b}} \, {\rm{ d}} S \phi _S(S) \int _{M_\text{a}}^{M_\text{b}} \, {\rm{ d}} M \phi _M(M)~, \end{eqnarray}$$where ϕS(S) is the number density function associated with S and $\phi _\mathit {M}(\mathit {M})$ is the mass function presented in Fig. 7. Assuming a probable range of S = 0.335 ± 0.06 and $1 \lt M \lt 2 \times 10^{15} \, {\rm M}_{\odot }$ we estimate a number density ∼4.92 × 10−10 Mpc−3 or that an effective volume of ∼2.03 Gpc3 would be required to observe a single Bullet-like cluster. This result is inline with the number density estimate of the order of ∼10−10 Mpc−3 by Thompson et al. (2015), which improves on previous estimates (e.g. Lee & Komatsu 2010; Thompson & Nagamine 2012; Bouillot et al. 2015) due to more sophisticated halo finding methods (e.g. Behroozi, Wechsler & Wu 2013). Conversely, it was estimated by Kraljic & Sarkar (2015) (utilizing the same halo finder as Thompson et al. 2015) that given an effective volume of ∼14.6 Gpc3, no Bullet cluster analogues are expected, however as indicated by a pairwise velocity distribution it would be expected that present binary halo–halo orbits have the potential to form a Bullet-like object.

2022MNRAS.516.1539O__Fujita,_Ohira_&_Yamazaki_2013_Instance_1

In this scenario, the present γ-ray emission from the Fermi bubbles arises predominantly through inverse Compton scattering of an energetic non-thermal cosmic ray (CR) electron population in the remnant structures with ambient radiation supplied by the interstellar radiation field (ISRF) and the cosmological microwave background (CMB). A subdominant component due to non-thermal bremsstrahlung may also be present, emitted primarily from regions of high gas and non-thermal electron density within the bubble (OY22). Models of this nature are broadly referred to as ‘leptonic’ models (see also Su et al. 2010; Zubovas, King & Nayakshin 2011; Su & Finkbeiner 2012; Fujita, Ohira & Yamazaki 2013 for similar configurations). Other similar approaches invoking Sgr A* activity, but where the CR composition is not specifically required to be leptonic, invoke a pair of jet-driven outflowing bubbles assuming constant AGN activity or continuous energy injection over Myr time-scales (e.g. Zhang & Guo 2020). Notably, these models have been able to account for the bi-conical X-ray structures observed near the GC as part of the same phenomenon as the Fermi bubbles. Alternative proposals have also been discussed, where the bubbles arise from the confluence of a number of processes operating more gradually within the inner part of the Milky Way (Thoudam 2013). These could include tidal disruption events (TDEs) occurring at regular intervals of 10s to 100s kyr (Cheng et al. 2011; Ko et al. 2020), or the action of a bipolar galactic outflow driven by the ongoing intense GC star-formation activity and/or the processes associated with Sgr A* (Lacki 2014), with the resulting γ-ray glow instead arising from a hadronic CR population interacting with an advected supply of entrained gas in the wind (the ‘hadronic’ models – see Crocker & Aharonian 2011; Cheng et al. 2014, 2015; Crocker et al. 2014, 2015; Mou et al. 2014, 2015; Razzaque & Yang 2018).

2017MNRAS.464L..26F__O'Sullivan_et_al._2001_Instance_2

The diffuse hot gas X-ray luminosities in the 0.3–8 keV band are taken from the work of KF15. They have carefully removed the contribution from discrete sources such as low-mass X-ray binaries (Fabbiano 2006) to the total X-ray luminosity, leaving the diffuse gas contribution LX, Gas. A correction to bolometric would increase the X-ray luminosities by 0.08 dex on average. Most of the X-ray data come from Chandra observations. However, for some high-mass galaxies, the X-ray emission is particularly extended (e.g. NGC 4374, 4486, 4649, 5846) and in those cases ROSAT data from O‘Sullivan et al. (2001), corrected to the Chandra energy band, are used. Although the contribution from discrete sources in the ROSAT data cannot be subtracted as accurately as it can for Chandra data, their contribution is only about 1 per cent of the diffuse gas luminosity for these high-mass galaxies (see O'Sullivan et al. 2001). For further details, see KF15. Here we make a very small correction to the KF15 LX,Gas luminosities for the distances used in the SLUGGS survey (Brodie et al. 2014). The KF15 compilation did not include several galaxies that appear in the Alabi et al. (in preparation) study. Here we also include the X-ray luminosities for NGC 720, NGC 1316 and NGC 3115 from Boroson et al. (2011), and for NGC 5128 from KF13. Su et al. (2014) conducted a detailed XMM and Chandra study of NGC 1400. As well as the X-ray emission centred on NGC 1400, they detected an enhanced region of X-rays to the NE of the galaxy that they associated with stripped gas. Here we use the X-ray luminosity centred on NGC 1400 with a small adjustment to our X-ray band and distance, and assume an uncertainty of 20 per cent. We note that the X-ray luminosity would double if the enhanced region were also included. Two galaxies in Alabi et al. (in preparation) but not included here are NGC 2974 (not observed by Chandra) and NGC 4474 (the Chandra observation was only 5 ks).

2022MNRAS.517.4327M__Indebetouw_et_al._2014_Instance_1

Supernovae (SNe) play a dual role in the evolution of interstellar dust. On one hand, they are the most important source of dust production in galaxies, but on the other had had also the most important source of grain destruction. Theoretical models show that most of the heavy elements produced can precipitate out of the gas and form refractory grains (Sarangi & Cherchneff 2013, 2015; Sluder, Milosavljević & Montgomery 2018; Sarangi, Matsuura & Micelotta 2019). Infrared and submilimetre observations of Cassiopeia A (Barlow et al. 2010; Arendt et al. 2014; De Looze et al. 2017), SN 1987A (Matsuura et al. 2011; Indebetouw et al. 2014; Matsuura et al. 2015), Crab Nebula (Gomez et al. 2012), and young Galactic (up to ∼2000 yr old) SN remnants (Chawner et al. 2019) confirm the presence of ∼0.1–1.0 M⊙ of dust, indicating that a substantial fraction of refractory elements in their ejecta went to dust grains. If the majority of dust in SNe can survive the shock interactions, SNe could be an important source of dust production in the ISM (Dwek & Cherchneff 2011). The fate of this newly-formed dust is still a subject of active studies. The reverse shock traveling through the ejecta can destroy newly-formed dust (Dwek, Foster & Vancura 1996; Schneider, Ferrara & Salvaterra 2004; Nozawa et al. 2007; Biscaro & Cherchneff 2014, 2016; Silvia, Smith & Shull 2010; Micelotta, Dwek & Slavin 2016; Kirchschlager et al. 2019). Any grains surviving the reverse shock may also be destroyed during the injection phase into the interstellar medium (ISM; Slavin et al. 2020) Thereafter, ISM dust will be subject to destruction as it encounters the SN remnant shocks. The grain destruction efficiency and ISM dust lifetimes are highly uncertain since they depend on a long list of parameters. Macroscopic parameters include the energy of the SN explosion, the morphology of the medium surrounding the SN (Slavin et al. 2020), and that of the general ISM. Microscopic parameters include the composition and size distribution of the SN condensates, and the detailed interaction of the dust with the shocked gas and other grains (Dwek & Arendt 1992; Jones, Tielens & Hollenbach 1996; Slavin, Dwek & Jones 2015; Kirchschlager, Mattsson & Gent 2021; Priestley, Chawner & of 2021). Because the evolution of dust in the ISM is a fine balance between dust production and destruction, intense investigations are currently underway into dust production and destruction by SNe. In this paper, we examine the latter point of view, and investigate how dust grains are impacted by SN shocks over time.

2019MNRAS.482.4290H__Hofmann_2017_Instance_1

The only non-standard term involves the factor $\frac{\mathrm{d} q}{\mathrm{d} \eta }$. As usual, the perturbation Ψ of the unperturbed phase-space distribution f0 = 1/(exp (ε/T0) − 1) is introduced as (36) \begin{eqnarray*} f = f_0(\epsilon) \left(1 + \Psi \right)\, , \end{eqnarray*} where the co-moving energy ε reads (37) \begin{eqnarray*} \epsilon \equiv \left(q^2 + a^2 \frac{m_{V_\pm }^2}{{\cal S}^2\, T_0^2}\right)^{1/2}\, . \end{eqnarray*} The scaling function $\mathcal {S}$ is defined in equation (5). Since the term $\frac{\mathrm{d} q}{\mathrm{d} \eta }$ is determined by the geodesic equation for a massive point particle, we may write (38) \begin{eqnarray*} \frac{\mathrm{d} q}{\mathrm{d} \eta } \frac{\partial f}{\partial q} &=& \left(q \dot{\phi } - \epsilon n_i \partial _i \psi - \frac{a^2 m_{V_\pm } \dot{m}_{V_\pm }}{q} \right)\nonumber\\ && \times \,\left(\frac{\partial f_0}{\partial q} (1 + \Psi) + f_0 \frac{\partial \Psi }{\partial q}\right). \end{eqnarray*} The use of the geodesic equation for a quasi-particle must be questioned, if this particle associates with pure quantum fluctuations (Hofmann 2017). If at all, temperature fluctuations in the V± sector can thus only be coherently propagated via the low-frequency regime of γ fluctuations in terms of classical electromagnetic waves (Hofmann 2016b). To do this, a residual interaction between V± and γ is required. Albeit when such an interaction occurs (Hofmann 2016a), its efficiency in conveying the coherent propagation of V± temperature fluctuations must be questioned, especially at high temperatures (Falquez et al. (2010). To ignore the V± Boltzmann equations and associated source terms in linearized Einstein equations thus is a physically motivated option. On the other hand, considering the evolution of V± temperature fluctuations via the coherently propagating low-frequency sector in γ implies that ε = q in the V± geodesic equation. At the same time, $m_{V_\pm }\gt 0$ is required in f0. Since the structure of temperature fluctuations is mainly imprinted before and during recombination, setting $m_{V_\pm }=0$ in the geodesic equation does not influence the prediction for the power spectra in practice. Note that due to equation (37) an explicit dependence of f0 on η needs to be considered via a = a(η). Transforming equation (35) into k space and otherwise following the standard procedure of linear perturbation theory (Ma & Bertschinger 1995), one arrives at the following hierarchy: (39) \begin{eqnarray*} \dot{\Psi }_0 = - k\Psi _1 - \frac{\mathrm{d} \ln f_0}{\mathrm{d} \ln q}\dot{\phi } - \frac{1+\Psi _0}{f_0} \frac{\partial f_0}{\partial \eta }\, , \end{eqnarray*} (40) \begin{eqnarray*} \dot{\Psi }_1 = \frac{k}{3 }(\Psi _0 - 2 \Psi _2) - \frac{1}{3} \frac{\mathrm{d} \ln f_0}{\mathrm{d} \ln q} k \psi - \frac{\Psi _1}{f_0} \frac{\partial f_0}{\partial \eta }, \end{eqnarray*} (41) \begin{eqnarray*} \dot{\Psi _l} = \frac{k}{(2l+1)}\left[l \Psi _{l-1}-(l+1)\Psi _{l+1} \right] -\frac{\Psi _l}{f_0} \frac{\partial f_0}{\partial \eta }, \end{eqnarray*} where the $\Psi _l(\vec{k},q,\eta)$ are the expansion coefficients for $\Psi (\vec{k},\hat{n},q,\eta)$ into Legendre polynomials.

2022AandA...663A..50B__Buat_et_al._2005_Instance_1

In the absence of dust, the spectral emission of a normal star-forming galaxy is dominated by stellar populations of different ages with superimposed nebular emission, mainly in the form of recombination lines as well as continuum. The interaction with dust has a dramatic effect, both dimming and reddening the emission from stars and ionized gas. This negatively impacts our ability to measure star formation as energetic photons produced by massive young stars are far more easily attenuated than longer wavelength photons, and even a small quantity of dust can lead to a significant attenuation in the ultraviolet (UV). In the case of particularly dust-rich galaxies, it can render their detection in the rest-frame UV especially difficult. However, as the FUV emission vanishes due to dust attenuation, this dust re-emits the absorbed energy in the mid-infrared (MIR) and far-infrared (FIR), which can in turn be exploited to trace star formation. Except for the most extreme cases (e.g., when the dust content is negligible or, conversely, when almost all of the UV photons are absorbed by dust), an attenuation correction must be carried out to retrieve the star formation rate (SFR). One of the most direct ways is to simply apply a hybrid SFR estimator combining the rest-frame UV with the IR (e.g., Hao et al. 2011; Boquien et al. 2016). The obvious downside is that this requires observations of the dust emission that are costly and difficult to obtain, and even more so at increasing redshifts, where they tend to be limited to vanishingly small samples. With the rest-frame UV emission being relatively easy to obtain from the ground from z ∼ 2 and beyond, techniques have been developed to relate the UV slope (β) to the UV attenuation (the IRX-β relation). While this approach initially appeared to work remarkably well in the case of starburst galaxies (Meurer et al. 1999), there is now ample evidence that there is no tight universal relation between the UV slope and the attenuation (e.g., Buat et al. 2005; Seibert et al. 2005; Howell et al. 2010; Casey et al. 2014). In fact, this relation relies on two strong underlying assumptions: the intrinsic UV slope of the stellar populations in the absence of dust and the exact shape of the attenuation curve. Numerous studies have analyzed their respective impact in an attempt to understand why and when such relations fail and build more reliable ones (e.g., Kong et al. 2004; Boquien et al. 2009, 2012; Popping et al. 2017, and many others). In particular, the recent study of Salim & Boquien (2019) found that the diversity of attenuation curves is a strong driver of the scatter around the IRX-β relation. This finding, which is consistent with simulations (Narayanan et al. 2018b; Liang et al. 2021), is especially important in that we can observe a broad variety of attenuation curves at all redshifts (e.g., Salmon et al. 2016; Buat et al. 2018; Salim et al. 2018). With the shape of the attenuation curve being strongly dependent on the relative geometry of stars, ionized gas, and dust (Salim & Narayanan 2020), from the disturbed morphologies observed at higher redshifts, we can only expect important variations there as well (e.g., Faisst et al. 2017). However, due to the great difficulty in measuring them and given the sparsity of the data available, our knowledge of attenuation curves beyond z = 4 remains limited. In effect, most observational studies on the attenuation properties of distant galaxies tend to concentrate on redshifts between 2 and 4 (e.g., Noll et al. 2009b; Buat et al. 2012, 2019; Reddy et al. 2012, 2015; Shivaei et al. 2015; Álvarez-Márquez et al. 2016; Salmon et al. 2016; Fudamoto et al. 2017, 2020b; Lo Faro et al. 2017; Álvarez-Márquez et al. 2019; Reddy et al. 2018; Koprowski et al. 2020). There is only a handful of examples at higher redshift (Capak et al. 2015; Scoville et al. 2015; Bouwens et al. 2016; Barisic et al. 2017; Koprowski et al. 2018). Because of the inherent limits of the observations, studies based on numerical simulations of galaxies at very high redshift (e.g., Mancini et al. 2016; Cullen et al. 2017; Di Mascia et al. 2021) are an important source of information. However, they lead to contrasted results, finding both flat (Cullen et al. 2017) and steep (Mancini et al. 2016) attenuation curves.

2019AandA...622A..62A__Aviles_et_al._(2018)_Instance_2

On the other hand, PT has experienced many developments in recent years (Matsubara 2008a; Baumann et al. 2012; Carlson et al. 2013), in part because it can be useful to analytically understand different effects in the power spectrum and correlation function for the dark matter clustering. These effects can be confirmed or refuted, and further explored with simulations to ultimately understand the outcomes of present and future galaxy surveys, such as eBOSS (Zhao 2016), DESI (Aghamousa et al. 2016), EUCLID (Amendola et al. 2013), and LSST (LSST Dark Energy Science Collaboration 2012). Two approaches have been used to study PT: the Eulerian standard PT (SPT) and Lagrangian PT (LPT), which both have advantages and drawbacks, but they are complementary in the end (Tassev 2014). The nonlinear PT for MG was developed initially in (Koyama et al. 2009), and has been further studied in several other works (Taruya et al. 2014a,b; Brax & Valageas 2013; Bellini & Zumalacarregui 2015; Taruya 2016; Bose & Koyama 2016, 2017; Barrow & Mota 2003; Akrami et al. 2013; Fasiello & Vlah 2017; Aviles & Cervantes-Cota 2017; Hirano et al. 2018; Bose et al. 2018; Bose & Taruya 2018; Aviles et al. 2018). The LPT for dark matter fluctuations in MG was developed in Aviles & Cervantes-Cota (2017), and further studies for biased tracers in Aviles et al. (2018). The PT for MG has the advantage that it allows us to understand the role that these physical parameters play in the screening features of dark matter statistics. We here study some of these effects through screening mechanisms by examining them at second- and third-order perturbation levels using PT for some MG models. To this end, we build on the LPT formalism developed in Aviles & Cervantes-Cota (2017), which was initially posited for MG theories in the Jordan frame, in order to apply it to theories in the Einstein frame. Because of the direct coupling of the scalar field and the dark matter in the Klein–Gordon equation, the equations that govern the screening can differ substantially from those in Jordan-frame MG theories. In general, screening effects depend on the type of nonlinearities that are introduced in the Lagrangian density. We present a detailed analysis of screening features and identify the theoretical roots of their origin. Our results show that screenings possess peculiar features that depend on the scalar field effective mass and couplings, and that may in particular cases cause anti-screening effects in the power spectrum, such as in the symmetron model. We perform this analysis by separating the growth functions into screening and non-screened parts. We note, however, that we do not compare the perturbative approach with a fully nonlinear simulation. We refer to (Koyama et al. 2009), for instance, for investigations like this at the level of the power spectrum.

2016ApJ...833...51Y__Liu_et_al._2012_Instance_1

The past two decades have seen rapid progress in the field of solar magnetoseismology (SMS; for recent reviews, see e.g., Nakariakov & Verwichte 2005; Banerjee et al. 2007; De Moortel & Nakariakov 2012; Nakariakov et al. 2016; Wang 2016). Among the rich variety of low-frequency waves observed in the Sun’s atmosphere, flare-related quasi-periodic fast propagating wave trains have received much attention (see Liu & Ofman 2014, for a recent review). Their quasi-periods usually range from 25 to 400 s. These wave trains were discovered (Liu et al. 2011) and extensively observed in images acquired with the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory (SDO/AIA; Liu et al. 2012; Shen et al. 2013; Yuan et al. 2013; Nisticò et al. 2014; also see Lemen et al. 2012 for a description of the instrument). On the other hand, quasi-periodic signals in coronal emissions presumably from density-enhanced loops have been known since the 1960s (e.g., Frost 1969; Parks & Winckler 1969; Rosenberg 1970; McLean & Sheridan 1973; see Table 1 of Aschwanden et al. 1999 for a comprehensive compilation of measurements prior to 2000). The quasi-periods P of a considerable fraction of these signals were of the order of seconds. While these measurements were largely spatially unresolved, more recent high-cadence instruments imaging the corona at total eclipses indicated the presence in coronal loops of quasi-periodic signals both with s (Williams et al. 2001, 2002; Katsiyannis et al. 2003) and with s (Samanta et al. 2016). In addition, quasi-periodic pulsations in the lightcurves of solar flares with similar periods have also been measured using imaging instruments such as the Nobeyama Radioheliograph (e.g., Asai et al. 2001; Nakariakov et al. 2003; Melnikov et al. 2005; Kupriyanova et al. 2013), SDO/AIA (e.g., Su et al. 2012), and more recently with the Interface Region Imaging Spectrograph (IRIS; Tian et al. 2016; see also De Pontieu et al. 2014 for a description of IRIS).

2018AandA...619A..13V__Saviane_et_al._2012_Instance_5

The EWs were measured with the methods described in Vásquez et al. (2015). As in Paper I, we used the sum of the EWs of the two strongest CaT lines (λ8542, λ8662) as a metallicity estimator, following the Ca II triplet method of Armandroff & Da Costa (1991). Different functions have been tested in the literature to measure the EWs of the CaT lines, depending on the metallicity regime. For metal-poor stars ([Fe/H] ≲ −0.7 dex) a Gaussian function was used with excellent results (Armandroff & Da Costa 1991), while a more general function (such as a Moffat function or the sum of a Gaussian and Lorentzian, G + L) is needed to fit the strong wings of the CaT lines observed in metal-rich stars (Rutledge et al. 1997; Cole et al. 2004). Following our previous work (Gullieuszik et al. 2009; Saviane et al. 2012) we have adopted here a G+L profile fit. To measure the EWs, each spectrum was normalised with a low-order polynomial using the IRAF continuum task, and set to the rest frame by correcting for the observed radial velocity. The two strongest CaT lines were fitted by a G+L profile using a non-linear least squares fitting routine part of the R programming language. Five clusters from the sample of Saviane et al. (2012) covering a wide metallicity range were re-reduced and analysed with our code to ensure that our EWs measurements are on the same scale as the template clusters used to define the metallicity calibration. Figure 5 shows the comparison between our EWs measurements and the line strengths measured by Saviane et al. (2012) (in both cases the sum of the two strongest lines) for the five calibration clusters. The observed scatter is consistent with the internal errors of the EW measurements, computed as in Vásquez et al. (2015). The measurements show a small deviation from the unity relation, which is more evident at low metallicity. A linear fit to this trend gives the relation: ΣEW(S12) = 0.97 ΣEW(this work) + 0.21, with an rms about the fit of 0.13 Å. This fit is shown in Fig. 5 as a dashed black line. For internal consistency, all EWs in this work have been adjusted to the measurement scale of Saviane et al. (2012) by using this relation. In Table 3 we provide the coordinates, radial velocities, and the sum of the equivalent widths for the cluster member stars, both measured (“m”) and corrected (“c”) to the system of Saviane et al. 2012.

2020MNRAS.498..464F__Alam_et_al._2017_Instance_1

The late-time matter density PDF at a given smoothing scale is mostly sensitive to the skewness of the primordial density field at that scale and to the running of that skewness around the smoothing scale. As such – unless the PDF is measured on a wide range of smoothing scales – it can only poorly distinguish between different primordial bispectrum shapes. Any model that produces mainly one of the possible bispectrum template can however be successfully tested with PDF measurements. In this paper, we consider an analysis of the PDF at redshift z = 1 in a survey volume of V = 100(Gpc h−1)3, which is smaller than the effective volume of upcoming surveys such as Spherex with Veff ≈ 150(Gpc h−1)3 and somewhat larger than existing surveys such as BOSS with Veff ≈ 55(Gpc h−1)3 (Doré et al. 2014; Alam et al. 2017). At a smoothing scale of 30 Mpc h−1 we find our PDF model to agree with the high-resolution run of the Quijote N-body simulations (Villaescusa-Navarro et al. 2019) to $\lesssim 0.2{{\ \rm per\ cent}}$ accuracy over a range of δ[30 Mpc h−1] ∈ [−0.3, 0.4]. This is within cosmic variance of the considered volume of 100(Gpc h−1)3 (which is also the combined volume of the Quijote high-resolution boxes). Restricting to this smoothing scale and to these mildly non-linear densities we find that a PDF based analysis can measure the amplitude of different primordial bispectrum shapes to an accuracy of $\Delta f_{\mathrm{NL}}^{\mathrm{loc}} = \pm 7.4\ ,\ \Delta f_{\mathrm{NL}}^{\mathrm{equi}} = \pm 22.0\ ,\ \Delta f_{\mathrm{NL}}^{\mathrm{ortho}} = \pm 46.0$ – even when marginalizing over the non-linear variance of the density field as a free parameter. When pushing to smaller scales and assuming a joint analysis of the PDF with smoothing radii of 30  and 15 Mpc h−1 (δ[15 Mpc h−1] ∈ [−0.4, 0.5]) this improves to $\Delta f_{\mathrm{NL}}^{\mathrm{loc}} = \pm 3.3\ ,\ \Delta f_{\mathrm{NL}}^{\mathrm{equi}} = \pm 11.0\ ,\ \Delta f_{\mathrm{NL}}^{\mathrm{ortho}} = \pm 17.0$ – even when marginalizing over the non-linear variances at both scales as two free parameters. Especially, such an analysis can simultaneously measure fNL and the amplitude and slope of the non-linear power spectrum. Note that any dependence of these forecasts on σ8 is completely mitigated by this marginalization. We do not consider the impact of Ωm on our signals (see Uhlemann et al. 2019, for an investigation of the general cosmology dependence of the PDF) though Friedrich et al. (2018) and Gruen et al. (2018) have demonstrated that parameters of the ΛCDM model and higher order moments of the density field can be measured simultaneously from what they call lensing-around-cells. Ultimately, we are working towards a combination of a late-time PDF analysis with the early-universe results of Planck Collaboration IX (2019). These two analyses have the potential to complement each other: the CMB providing information about the background ΛCDM space–time, the late-time density PDF providing information about non-linear structure growth and both of them containing independent information about the imprint of primordial non-Gaussianities on the large-scale structure.

2019ApJ...871..176X__Eldridge_et_al._2013_Instance_1

The progenitors of SNe Ib/c have been thought to be Wolf-Rayet (W-R) stars with high initial masses (MZAMS ≳ 25 M⊙; Crowther 2007). Before core collapse, these stars usually have experienced severe mass loss through strong stellar winds or due to interaction with companion stars (van der Hucht 2006; Paxton et al. 2015). As the evolution of massive stars is usually dominated by binary evolution (Heger et al. 2003) and also depends largely on metallicity, rotation, and so on (Heger et al. 2003; Georgy et al. 2013, 2012), this makes the direct identification of their progenitors complicated (Smartt 2015). However, there are increasing studies suggesting that a lower-mass binary scenario is more favorable for most SNe Ib/c, considering the measured low ejecta masses (Eldridge et al. 2013; Lyman et al. 2016). In addition, the H/He envelopes of the progenitor stars are stripped by binary interaction. There are many detections of progenitor stars for SNe II. For example, most SNe IIP are found to originate from red supergiants (Smartt et al. 2009), while SNe IIL are typically from progenitors with somewhat warmer colors (see Smartt 2015, for a review), and SNe IIb are from those with higher effective temperatures such as yellow supergiants that have had their H/He envelopes partially stripped through binary interaction (e.g., SN 1993J; Podsiadlowski et al. 1993; Maund et al. 2004; Fox et al. 2014). Until recently, there has been only one report of the possible identification of a progenitor star for SNe Ib, namely, iPTF 13bvn, which was proposed to spatially coincide with a single W-R-like star identified on the pre-explosion Hubble Space Telescope (HST) images (Cao et al. 2013; Groh et al. 2013). But such an identification is still controversial (e.g., Bersten et al. 2014; Fremling et al. 2014; Eldridge et al. 2015; Eldridge & Maund 2016). Direct detection of progenitor stars is still elusive for SNe Ic, which prevents us from further testing the theoretical evolution of massive stars (Eldridge et al. 2013).

2022MNRAS.513..232N__Hayden_et_al._2015_Instance_1

There are a plethora of data available in the form of spectra, astrometric, and photometric information, as well as multiwavelength maps with the advent of large-scale spectroscopic (Apache Point Observatory Galactic Evolution Experiment/APOGEE: Eisenstein et al. 2011, RAdial Velocity Experiment/RAVE: Steinmetz et al. 2006, Gaia-ESO: Gilmore et al. 2012, Large Sky Area Multi-Object Fiber Spectroscopic Telescope/LAMOST: Cui et al. 2012, Galactic Archaeology with HERMES/GALAH: De Silva et al. 2015, Abundances and Radial velocity Galactic Origins Survey/ARGOS: Ness et al. 2012), astrometric (Hipparcos: Perryman et al. 1997, Gaia: Gaia Collaboration 2016), and photometric surveys (Two-Micron All Sky Survey/2MASS: Skrutskie et al. 2006, Sloan Digital Sky Survey/SDSS: Stoughton et al. 2002, Vista Variables in the Vía Láctea/VVV: Minniti et al. 2010, the SkyMapper Southern Survey : Wolf et al. 2018). These surveys have enabled the chemo-dynamic characterization of stellar populations in the Milky way that constitute different Milky Way components like thin disc, thick disc, halo, bulge, etc. For example, star count observations in the solar neighbourhood (Yoshii 1982; Gilmore & Reid 1983) led to the discovery of the thick disc, followed by its characterization as the old α-enhanced population in the double sequence exhibited by the solar neighbourhood stars in the [α/Fe] versus [Fe/H] plane (Fuhrmann 1998; Bensby, Feltzing & Lundström 2003; Reddy, Lambert & Allende Prieto 2006; Adibekyan et al. 2012; Haywood et al. 2013). At present, data from large-scale spectroscopic surveys (Anders et al. 2014; Hayden et al. 2015; Weinberg et al. 2019) have led to the discovery of this trend at different galactocentric radius, R, and average height, |Z|, across the Galaxy shedding light on the disc formation and evolution scenarios. In addition, many age determination methods have been developed that uses these survey data to provide valuable information about the star formation histories and age metallicity relation of disc stellar populations (Casagrande et al. 2011; Bedell et al. 2018; Lin et al. 2020; Nissen et al. 2020). Secular processes such as radial migration (Sellwood & Binney 2002; Schönrich & Binney 2009; Minchev & Famaey 2010), which leads to the mixing of stars across the Galaxy, are also being explored using a combination of accurate phase space information from Gaia (Gaia Collaboration 2018) and chemistry and age information of stars from large-scale spectroscopic surveys (Buder et al. 2019). The discovery of streams and dynamically different stellar populations in the Milky Way halo, considered to be the result of past accretion/merger events (Belokurov et al. 2018; Helmi et al. 2018; Ibata, Malhan & Martin 2019; Myeong et al. 2019) using the Gaia data and their further exploration with chemistry from large-scale spectroscopic surveys (Buder et al. in preparation) is another example. Multiple components in the Bulge metallicity distribution function discovered by multiple individual and large-scale spectroscopic observations, are being studied in detail to understand the origin of the Bulge and its connection with the Milky Way bar and Galaxy evolution (Ness et al. 2013; Rojas-Arriagada et al. 2017, 2020). There are many upcoming surveys [4-metre Multi-Object Spectroscopic Telescope/4MOST: de Jong et al. (2019), Sloan Digital Sky Survey/SDSS-V: Kollmeier et al. (2017), WEAVE: Dalton et al. (2018)] that will further improve our understanding of the formation and evolution of the Milky Way and its components.

2021AandA...651A.111P__Herrera-Camus_et_al._2018_Instance_2

Irrespective of its origin, the [C II] emission is linked to the presence of stellar far-ultraviolet (FUV) photons (E 13.6 eV). As FUV photons are tied to the presence of massive O and B stars that have short lifetimes, the [C II] 158 μm line is also astar formation rate (SFR) indicator. Indeed, ISO, Herschel and SOFIA observations have demonstrated the good correlation between the [C II] luminosity and the SFR in the Milky Way and in regions of massive star formation in other galaxies (e.g., Kramer et al. 2013, 2020; Pineda et al. 2014, 2018; Herrera-Camus et al. 2015, 2018; De Looze et al. 2011). With ALMA and NOEMA, ground-based observations of the [C II] 158 μm line in high redshift galaxies have come into reach and such data are routinely used to infer SFRs (e.g., Walter et al. 2012; Venemans et al. 2012; Knudsen et al. 2016; Bischetti et al. 2018; Khusanova et al. 2021) based upon validations of this relationship in the nearby Universe (Herrera-Camus et al. 2018; De Looze et al. 2011). However, it is well-understood that the intensity of the [C II] line depends on the local physical conditions (Hollenbach & Tielens 1999). Observationally, the presence of the so-called [C II]-deficit – a decreased ratio of [C II] 158 μm luminosity to FIR dust continuum with an increasing dust color temperature and also with FIR luminosity – is well established (Malhotra et al. 2001; Díaz-Santos et al. 2013; Magdis et al. 2014; Smith et al. 2017). This deficit is particularly pronounced in (local) ultraluminous infrared galaxies (ULIRGs), very dusty galaxies characterized by vigorous embedded star formation (e.g., Luhman et al. 2003; Abel et al. 2009; Graciá-Carpio et al. 2011). This deficit, however, does not necessarily hold in the early Universe at high redshift (e.g., Stacey et al. 2010; Brisbin et al. 2015; Capak et al. 2015). Some studies have indicated that not only [C II] emission is deficient in some sources, but other FIR cooling lines ([O I], [O III], [N II]), as well (e.g., Graciá-Carpio et al. 2011; Herrera-Camus et al. 2018). These deficits must be linked to the global ISM properties and star-formation characteristics in these galaxies.

2020ApJ...903L..12H__Petschek_1964_Instance_1

Magnetic reconnection (MR) may occur in various space and astrophysical plasma environments, among which the planetary magnetopause boundaries separating the solar wind and magnetospheric origins of plasmas and magnetic field are some of the most likely sites for the occurrence of MR. Due to the easy access to the in situ spacecraft observations the Earth’s magnetopause is the most widely studied space plasma environment for MR (Paschmann et al. 1979; Vaivads et al. 2004; Graham et al. 2014). In particular, the Magnetospheric Multiscale (MMS) mission has contributed greatly to the kinetic physics of magnetopause reconnection (Burch et al. 2016; Hasegawa et al. 2017; Zhong et al. 2020). Many studies have shown that an initial Harris type equilibrium profile with constant total pressures and antiparallel magnetic field with or without a guide field (Harris 1962) may tend to develop MR geometry. In particular, two major categories of MR have been proposed: the steady state model with a single X line and the outflow approaching the Alfvén speed (Petschek 1964), and the tearing mode instability with a series of X and O lines and mild plasma velocity (Furth et al. 1963). Numerous fluid and kinetic simulations have been carried out to examine the various aspects of MR processes for the past 50 yr (Hau & Chiou 2001; Guo et al. 2015; Landi et al. 2015). In particular, the effects of pressure or temperature anisotropy on MR have been examined by a number of authors (Chen & Palmadesso 1984; Shi et al. 1987; Birn & Hesse 2001; Chiou & Hau 2002, 2003; Hung et al. 2011). In the MHD models the double-polytropic (DP) laws are widely adopted as the energy closures to study the effects of temperature anisotropy and energy closures on MR and tearing mode instability (Chiou & Hau 2002, 2003; Hung et al. 2011). It is shown that the mirror type temperature anisotropy of may greatly enhance the growth rate of tearing mode instability and the merging rate of single X-line reconnection. In particular, the coupling of tearing and mirror instabilities may lead to relatively larger magnetic islands as compared to the cases with isotropic pressure and the mirror waves with anticorrelated density and magnetic field may be present in the vicinity of X lines.

2018AandA...616A.173K__Pickett_1991_Instance_1

The room-temperature millimeter wave rotational spectrum of ethyl isocyanate in Fig. 1 presents an exceptionally high line density. Its analysis was started using predictions from the spectroscopic constants obtained in the previous microwave works (Heineking et al. 1994; Kasten et al. 1983; Sakaizumi et al. 1976) for the stable cis configuration. It is characterized by a relatively large dipole moment along the a inertial axis (|µa| = 2.81 D and |µb| = 0.03 D (Sakaizumi et al. 1976), Cs symmetry, and dihedral angle τ(C–C–N = C) = 0◦ (see Fig. 1). The identification of Ka = 0 and lower-frequency Ka = 1 transitions, originating from J0 J and J1 J energy levels, was relatively straightforward and their assignment and analysis could be easily expanded up to 340 GHz (J˝ = 64) with the help of the Loomis-Wood-type plot technique from the AABS package (Kisiel et al. 2005) and SP-FIT/SPCAT program suite (Pickett 1991). The upper-frequency Ka = 1 transitions (J1 J−1 levels), which became degenerate with Ka = 2 transitions (J2 J−1 levels) for J˝ > 45, were subsequently localized, however, their analysis quickly ran into problems. As shown in Fig. 2, significant departures from the predicted positions were observed at high J˝ even though it was clear from the Loomis-Wood-type plots that the assignments were correct. Similar situations also occurred for higher Ka transitions and with increasing value of Ka, the limit of J up to which the rotational transitions were amenable to the simple semirigid-rotor Hamiltonian analysis, was decreasing (see Fig. 2). Only those transitions that supported the semirigid-rotor treatment were retained in the analysis. These transitions were finally combined with hyperfine-free transitions (5–24 GHz, J˝ = 0−7, K˝a = 0−3) from Heineking et al. (1994) and globally fitted using Watson’s S -reduced Hamiltonian in Ir representation. The adjusted rotational and centrifugal distortion constants are given in Table 1. The list of experimental frequencies is provided in the Table 4. Finally, the deviation trends observed in the Loomis–Wood type plots, such as those in Fig. 2, were advantageously followed to measure the frequencies of more than 200 transitions that could not be encompassed in the semirigid rotor fit. These transitions are collected in the Table 5.

2021MNRAS.500.5009M__Bono_et_al._2003_Instance_2

RR Lyrae are old low-mass stars that, during the central helium-burning phase, show mainly radial pulsation while crossing the classical instability strip in the colour–magnitude diagram. From the observational point of view, they represent the most numerous class of pulsating stars in the Milky Way and, being associated with old stellar populations, are typically found in globular cluster and abundant in the Galactic halo and bulge. The investigation of RR Lyrae properties is motivated by their important role both as distance indicators and tracers of old stellar populations. In particular, evolving through the central helium-burning phase, they represent the low-mass, Population II counterparts of Classical Cepheids, as powerful standard candles and calibrators of secondary distance indicators. In particular, they can be safely adopted to infer distances to Galactic globular clusters (see e.g. Coppola et al. 2011; Braga et al. 2016, 2018, and references therein), the Galactic centre (see e.g. Contreras Ramos et al. 2018; Marconi & Minniti 2018; Griv, Gedalin & Jiang 2019), and Milky Way satellite galaxies (see e.g. Coppola et al. 2015; Martínez-Vázquez et al. 2019; Vivas et al. 2019, and references therein). Being associated with old stellar populations, they represent the basis of an alternative Population II distance scale (see e.g. Beaton et al. 2016, to the traditionally adopted Classical Cepheids), more suitable to calibrate secondary distance indicators that are not specifically associated with spiral galaxies (e.g. the globular cluster luminosity function, see Di Criscienzo et al. 2006, and references therein). The properties that make RR Lyrae standard candles are (i) the well-known relation connecting the absolute visual magnitude MV to the metal abundance [Fe/H] (see e.g. Sandage 1993; Caputo et al. 2000; Cacciari & Clementini 2003; Catelan, Pritzl & Smith 2004; Di Criscienzo, Marconi & Caputo 2004; Federici et al. 2012; Marconi 2012; Marconi et al. 2015, 2018; Muraveva et al. 2018, and references therein); (ii) the period–luminosity relation in the near-infrared (NIR) filters and in particular in the K 2.2 μm band (see e.g. Longmore et al. 1990; Bono et al. 2003; Dall’Ora et al. 2006; Coppola et al. 2011; Ripepi et al. 2012; Coppola et al. 2015; Marconi et al. 2015; Muraveva et al. 2015; Braga et al. 2018; Marconi et al. 2018, and references therein). In spite of the well-known advantage of using NIR filters (see e.g. Marconi 2012; Coppola et al. 2015, and references therein), in the last decades there has been a debate on the coefficient of the metallicity term of the KB and PL relation (see e.g. Bono et al. 2003; Sollima, Cacciari & Valenti 2006; Marconi et al. 2015, and references therein). On the other hand, it is interesting to note that many recent determinations (see e.g. Sesar et al. 2017; Muraveva et al. 2018) seem to converge towards the predicted coefficient by Marconi et al. (2015), with values in the range 0.16-0.18 mag dex−1. As for the optical bands, our recently developed theoretical scenario (Marconi et al. 2015) showed that, apart from the MV−[Fe/H] relation that is affected by a number of uncertainties (e.g. a possible non-linearity, the metallicity scale with the associated α elements enhancement and helium abundance variations, as well as evolutionary effects, see Caputo et al. 2000; Marconi et al. 2018, for a discussion), the metal-dependent Period–Wesenheit (PW) relations are predicted to be sound tools to infer individual distances. In particular, for the B-, V- band combination, Marconi et al. (2015) demonstrated that the inferred PW relation is independent of metallicity. In order to test this theoretical tool, we need to compare the predicted individual distances with independent reliable distance estimates, for example, the astrometric ones recently obtained by the Gaia satellite (Gaia Collaboration 2016). To this purpose, in this paper we transform the predicted light curves derived for RR Lyrae models with a wide range of chemical compositions (Marconi et al. 2015, 2018) into the Gaia bands, derive the first theoretical PW relations in these filters and apply them to Gaia Data Release 2 Data base (hereinafter Gaia DR2; Gaia Collaboration 2018; Clementini et al. 2019; Ripepi et al. 2019). The organization of the paper is detailed in the following. In Section 2, we summarize the adopted theoretical scenario, while in Section 3 we present the first theoretical light curves in the Gaia filters. From the inferred mean magnitudes and colours, the new theoretical PW relations are derived in Section 4 that also includes a discussion of the effects of variations in the input chemical abundances. In Section 5, the obtained relations are applied to Gaia Galactic RR Lyrae with available periods, parallaxes, and mean magnitudes to infer independent predictions on their individual parallaxes, to be compared with Gaia DR2 results. The conclusions close the paper.

2022MNRAS.511.2105K__McElroy_et_al._2015_Instance_1

AGN feedback can exist in several forms such as radiation, thermal, or non-thermal (cosmic rays) pressure-driven winds, jet-mode feedback, and via magnetic forces on accretion disc scales. AGN feedback can explain several observed properties such as the presence of high velocity (>1000 km s−1) multiphase gas outflows in low and high redshift galaxies and observations of bubbles or cavities in X-ray observations of galaxy clusters (e.g. Blanton et al. 2011; Fabian 2012; Sanders et al. 2014; Feruglio et al. 2015; Laha et al. 2021). High velocity outflows from AGN host galaxies have been reported in numerous studies in the literature (see Veilleux et al. 2020 for a review and the references therein) using optical spectroscopy (e.g. Greene et al. 2011; McElroy et al. 2015; Sun, Greene & Zakamska 2017; Durré & Mould 2018; Manzano-King, Canalizo & Sales 2019; Perna et al. 2020; Santoro et al. 2020; Trindade Falcão et al. 2021), near-infrared spectroscopy (e.g. Kakkad et al. 2016; Zakamska et al. 2016; Bischetti et al. 2017; Diniz et al. 2019; Riffel, Zakamska & Riffel 2020a; Riffel et al. 2020b) and sub-mm spectroscopy (e.g. Michiyama et al. 2018; Zschaechner et al. 2018a; Audibert et al. 2019; Impellizzeri et al. 2019; García-Bernete et al. 2021). One of the key quantities that is not well understood through these observations is how efficiently does the outflow couple with the ISM (e.g. Harrison et al. 2018). The coupling efficiency i.e. the ratio between the kinetic power of the outflow ($\dot{E}_{\rm kin}$) and the bolometric luminosity of the AGN (Lbol) or the star formation rate (SFR) of the host galaxy is critical to quantify the true impact of AGN feedback on host galaxies – the higher the efficiency, the easier it is for these outflows to heat the gas or propagate the outflows to the galaxy outskirts. An accurate measurement of mass outflow rate and kinetic energy is therefore necessary to estimate the true coupling efficiency, which can also be used as constraints in cosmological simulations.

2015ApJ...808...56M__Beaulieu_et_al._2011_Instance_3

The field of extrasolar planetary transits is one of the most productive and innovative subject in astrophysics in the last decade. Transit observations can be used to measure the size of planets, their orbital parameters (Seager and Mallén-Ornelas 2003), and stellar properties (Mandel & Agol 2002; Howarth 2011), to study the atmospheres of planets (Brown 2001; Charbonneau et al. 2002; Tinetti et al. 2007), and to detect small planets (Miralda-Escudé 2002; Agol et al. 2005) and exomoons (Kipping 2009a, 2009b). In particular, the study of planetary atmospheres requires a high level of photometric precision, i.e., one part in ∼104 in stellar flux (Brown 2001), which is comparable to the effects of current instrumental systematics and stellar activity (Berta et al. 2011; Ballerini et al. 2012), hence the necessity of testable methods for data detrending. In some cases, different assumptions, e.g., using different instrumental information or functional forms to describe them, leed to controversial results even from the same data sets; examples in the literature are Tinetti et al. (2007), Ehrenreich et al. (2007), Beaulieu et al. (2008) and Désert et al. (2009, 2011) for the hot-Jupiter HD 189733b, and Stevenson et al. (2010), Beaulieu et al. (2011) and Knutson et al. (2011, 2014) for the warm-Neptune GJ436b. Some of these controversies are based on Spitzer/IRAC data sets at 3.6 and 4.5 μm. The main systematic effect for these two channels is an almost regular undulation with period ∼3000 s, so called pixel-phase effect, as it is correlated with the relative position of the source centroid with respect to a pixel center (Fazio et al. 2004; Morales-Caldéron et al. 2006). Conventional parametric techniques correct for this effect by dividing the measured flux by a polynomial function of the coordinates of the photometric centroid; some variants may include time-dependence (e.g., Stevenson et al. 2010; Beaulieu et al. 2011). Newer techniques attempt to map the intra-pixel variability at a fine-scale level, e.g., adopting spatial weighting functions (Ballard et al. 2010; Cowan et al. 2012; Lewis et al. 2013) or interpolating grids (Stevenson et al. 2012a, 2012b). The results obtained with these methods appear to be strongly dependent on a few assumptions, e.g., the degree of the polynomial adopted, the photometric technique, the centroid determination, calibrating instrument systematics over the out-of-transit only or the whole observation (e.g., Beaulieu et al. 2011; Diamond-Lowe et al. 2014; Zellem et al. 2014). Also, the very same method, applied to different observations of the same system, often leads to significantly different results. Non-parametric methods have been proposed to guarantee a higher degree of objectivity (Carter & Winn 2009; Thatte et al. 2010; Gibson et al. 2012; Waldmann 2012, 2014; Waldmann et al. 2013). Morello et al. (2014, 2015) reanalyzed the 3.6 and 4.5 μm Spitzer/IRAC primary transits of HD 189733b and GJ436b obtained during the cryogenic regime, so called “cold Spitzer” era, adopting a blind source separation technique, based on an Independent Component Analysis (ICA) of individual pixel timeseries, in this paper called “pixel-ICA”. The results obtained with this method are repeatable over different epochs, and a photometric precision of one part in ∼104 in stellar flux is achieved, with no signs of significant stellar variability as suggested in the previous literature (Désert et al. 2011; Knutson et al. 2011). The use of ICA to decorrelate the transit signals from astrophysical and instrumental noise, in spectrophotometric observations, has been proposed by Waldmann (2012, 2014) and Waldmann et al. (2013). The reason to prefer such blind detrending methods is twofold: they require very little, if any, prior knowledge of the instrument systematics and astrophysical signals, therefore they also ensure a higher degree of objectivity compared to methods based on approximate instrument systematics models. As an added value, they give stable results over several data sets, also in those cases where more conventional methods have been unsuccessful. Recently, Deming et al. (2015) proposed a different pixel-level decorrelation method (PLD) that uses pixel timeseries to correct for the pixel-phase effect, while simultaneously modeling the astrophysical signals and possible detector sensitivity variability in a parametric way. PLD has been applied to some Spitzer/IRAC eclipses and synthetic Spitzer data, showing better performances compared to previously published detrending methods.

2017AandA...608A..67B__Luu_(1991)_Instance_1

The large Jovian irregular satellites have been observed and analyzed using their light curves, colours, and reflectance spectra but the reported measurements are sometimes contradictory. The multicolour observation of some retrograde and prograde Jovian irregular satellites by Tholen & Zellner (1984) suggested C-class surface composition for prograde and more diverse colours for the retrograde families with a mixture of C- and D-type spectra. They noted that Carme had a flat visible wavelength reflectance spectrum, but with a strong upturn in the ultraviolet. Tholen & Zellner (1984) suggested that Carme might be showing low-level cometary activity with CN emission at 0.388 μm. Luu (1991) identified C- and D-type asteroid spectral features for both prograde and retrograde families based on spectroscopic observations of JV-JXIII and suggested them to be similar to Jupiter’s Trojan asteroids. Based on 1.3–2.4 μm near-IR (NIR) observations, Brown (2000) reported their compositions as being similar to P- and D-class asteroids from the outer asteroid belt, while their visible spectra resemble C-class asteroids. Cruikshank (1977) and Degewij et al. (1980) observed Himalia in the NIR and confirmed that its surface composition is similar to that of C-type asteroids. Brown (2000) concluded that NIR spectra of Himalia and Elara are featureless between 1.4 and 2.5 μm and do not contain any water-ice absorption features. Subsequently, Brown & Rhoden (2014) supported these findings and suggested that these objects lacked aqueously altered phyllosilicates based on the absence of a 3 μm absorption band. Brown et al. (2003) and Chamberlain & Brown (2004) studied Himalia using data acquired by the Visual and Infrared Mapping Spectrometer (VIMS) on-board Cassini spacecraft during Jupiter’s fly-by and found that its spectrum (0.3–5.1 μm) has low reflectance, a slight red slope, and an apparent absorption near 3-μm suggesting the presence of water in some form. In addition, Jarvis et al. (2000) reported a weak absorption at 0.7 μm in Himalia’s spectrum and attributed it to oxidized iron. Contrary to this result, Brown & Rhoden (2014) found no evidence for aqueously altered phyllosilicates in the 2.2–3.8 μm region.

2022AandA...661A..10B__Ghirardini_et_al._2021a_Instance_1

It is also possible that these clusters have a smaller extent and can just be missed by our extent selection as our detection algorithm sets the extent to zero if it is smaller than 6 (Brunner et al. 2022). Following the method presented in Ghirardini et al. (2021a), we estimated several dynamical properties of the clusters in the point source sample and compared them with the extent-selected sample presented in Ghirardini et al. (2021a). In Fig. 5 we compare the distributions of the core radii (Rcore) constrained by the V06 model and the concentration parameter (cSB) between these two samples. The concentration parameter is defined as the ratio of the surface brightness within 0.1 R500 to the surface brightness within R500 (Ghirardini et al. 2021a; Santos et al. 2008; Maughan et al. 2012). Intuitively, the expectation is that the smaller the core radius, the more compact the cluster. The left panel of Fig. 5 clearly shows that the clusters in the point source sample have relatively smaller core radii, hence the emission is more concentrated in a smaller area. Consistently, the concentration of the point source sample shows a clear excess in higher values than the extent-selected sample, indicating that a significantly larger fraction of cool-core clusters and clusters host a central AGN. We performed the same experiment by applying cuts in flux 1.5 × 10−14 ergs s−1 cm−2 and in detection likelihood to test whether the clusters are missed by the extent selection because they are fainter and/or more compact than the extent-selected clusters. The distribution of number density, core radius, and concentration parameter remains the same, indicating that the population of clusters in the point source catalog is more compact than the extent-selected sample. The extent-selected sample does not show a clear bias toward cool-core clusters or clusters with a central AGN, but contains the fraction of cool-cores is similar to that of SZ surveys (Ghirardini et al. 2021a). In this sample, we observe the opposite trend.

2018AandA...615L..16F__Hily-Blant_et_al._2013b_Instance_1

The observational and theoretical studies of nitrogen isotope fractionation in star-forming regions can help to constrain nitrogen chemistry. Nitrogen has two stable isotopes, 14N and 15N. The elemental abundance ratio [14N/15N]elem in the local interstellar medium (ISM) has been estimated to be ~200–300 from the absorption line observations of N-bearing molecules toward diffuse clouds (Lucas & Liszt 1998; Ritchey et al. 2015). L1544 is a prototypical prestellar core located in the Taurus molecular cloud complex. In L1544, the 14N/15N ratio of several different molecules has been measured: 14 N 2 H + = 920 − 200 + 300 , 14 N 2 H + / NNH + = 1000 − 220 + 260 $ {}^{14}{\mathrm N}_2\mathrm H^+=920_{-200}^{+300},^{14}{\mathrm N}_2\mathrm H^+/\mathrm{NNH}^+=1000_{-220}^{+260} $ (Bizzocchi et al. 2013; Redaelli et al. 2018), NH2D/15NH2D > 700 (Gérin et al. 2009), CN/C15N = 500 ± 75 (Hily-Blant et al. 2013b), and HCN/HC15N = 257 (Hily-Blant et al. 2013a). Among the measurements, the significant depletion of 15N in N2H+ is the most challenging for the theory of 15N fractionation. In general, molecules formed at low temperatures are enriched in 15N through gas-phase chemistry triggered by isotope exchange reactions (e.g., Terzieva & Herbst 2000). A 15N-bearing molecule has a slightly lower zero-point energy than the corresponding 14N isotopolog. This results in endothermicity for the exchange of 15N for 14N, which inhibits this exchange at low temperature enabling the concentration of 15N in molecules. Astrochemical models for prestellar cores, which consider a set of nitrogen isotope exchange reactions, have indeed predicted that atomic N is depleted in 15N, while N2 (and thus N2H+) is enriched in 15N (e.g., Charnley & Rodgers 2002). The model prediction clearly contradicts the observation of the N2H+ isotopologs in L1544. The 15N depletion in N2H+ was recently found in other prestellar cores as well, such as L183, L429, and L694-2 (Redaelli et al. 2018). Furthermore, Roueff et al. (2015) recently pointed out the presence of activation barriers for some key nitrogen isotope exchange reactions, based on their quantum chemical calculations. Then 15N fractionation triggered by isotope exchange reactions may be much less efficient than previously thought (Roueff et al. 2015, but see also Wirström & Charnley 2018).

2022MNRAS.515L..39Z__Koefoed_et_al._2016_Instance_1

Currently, this 53Mn–53Cr age of 4566.6 ± 0.6 Ma for crystallization for EC 002 represents the oldest record of volcanism in the Solar system. For example, the oldest crust formation of Earth and Moon only dates back to ∼4.3–4.4 Ga (O’Neil & Carlson 2017; Borg et al. 2019), and Mars, Vesta, and the angrite and main-group aubrite parent bodies show ages of mantle–crust differentiation at ∼4547 Ma (Bouvier et al. 2018), 4564.8 ± 0.6 Ma (Trinquier et al. 2008), 4563.2 ± 0.2 Ma (Zhu et al. 2019b), and 4562.5 ± 1.1 Ma (Zhu et al. 2020b), respectively. The crystallization age of EC 002 also predates all those of the other dated achondrites, such as angrites (Amelin 2008a, b; Connelly et al. 2008), ureilites (Goodrich et al. 2010; Bischoff et al. 2014), NWA 8704/6693, NWA 11119 (Srinivasan et al. 2018), and NWA 7325 (Koefoed et al. 2016). The result strongly supports the notion that advanced silicate differentiation occurred and evolved planetary crust formation very early in the Solar system, i.e. within the first 1 Ma after CAI formation (4567.3 ± 0.1 Ma; Amelin et al. 2010; Connelly et al. 2012). The crystallization of andesitic crust must post-date both accretion and core formation on the EC 002 parent body, which is also consistent with evidence for early core formation for some asteroids derived from some iron meteorites (Kruijer et al. 2014; Anand et al. 2021). The age for EC 002 is older than some of the chondrule formation ages (Connelly et al. 2012; Bollard et al. 2017; Zhu et al. 2020b). This observation supports previous suggestions that many chondrites and their components reflect younger nebular processes, post-dating the oldest differentiated planetesimals, such as the EC 002 parent body. Thus, chondrules may not necessarily reflect an important ingredient in the accretion history of terrestrial planets (Johansen et al. 2015), although this cannot be excluded for earlier chondrule precursors with older generations (Zhu et al. 2019a). Considering its very old age and the short half-life of 0.7 Ma of 26Al, the heat source for melting of the EC 002 parent body must have been the decay of 26Al. The reason why EC 002 cooled and crystallized so early might have been that its parent body was of a much smaller size than the terrestrial planets, since small bodies cannot retain their heat well. The size of the EC 002 parent body may have been smaller than the size of asteroids like Vesta (with mean radius of 262.7 km; Russell et al. 2012) and the angrite and aubrite (main-group) parent bodies, which differentiated later, at 2.5–5 Ma after CAIs (Amelin 2008a; Trinquier et al. 2008; Zhu et al. 2019b, 2021b).

2017MNRAS.464..968S__Tacconi_et_al._2006_Instance_2

Comparison of apparent effective diameters of these sources to direct size measurements supports a similar conclusion. Simpson et al. (2015) present ALMA observations of 23 SCUBA-2-selected SMGs with a median physical half-light diameter of 2.4 ± 0.2 kpc, while Ikarashi et al. (2015) show ALMA observations of 13 AzTEC-selected SMGs with a median physical half-light diameter of $1.34^{+0.26}_{-0.28}$ kpc. ALMA observations of four SPT-selected lensed SMGs give a mean physical half-light diameter of 2.14 kpc (Hezaveh et al. 2013b). This measurement is consistent with a recent lensing analysis of a significantly expanded SPT-selected DSFG sample (Spilker et al. 2016). These high-resolution ALMA observations constrain the FIR sizes of the sources to be 1.0–2.5 kpc. Earlier observations of the physical sizes of SMGs by CO detection and 1.4 GHz imaging suggest larger sizes (e.g. Tacconi et al. 2006; Biggs & Ivison 2008; Younger et al. 2008). However, Simpson et al. (2015) point out that the submillimetre sizes are consistent with resolved 12CO detections, while the sizes derived from 1.4 GHz imaging are about two times larger because of the cosmic ray diffusion, which can explain the results before higher frequency observations at ALMA were possible (Chapman et al. 2004; Tacconi et al. 2006; Biggs & Ivison 2008; Younger et al. 2008). Similarly, Ikarashi et al. (2015) reveal that the 12CO detected sizes and the 1.4 GHz imaging sizes of similar sources are greater than their submillimetre sizes as well. Furthermore, observations of local galaxies also show the submillimetre sizes are smaller than the CO detected sizes (e.g. Sakamoto et al. 2006, 2008; Wilson et al. 2008) and the 1.4 GHz continuum sizes (e.g. Elbaz et al. 2011). Our photometrically derived $\sqrt{\mu }d$ is best compared to the submillimetre continuum sizes. With a median apparent effective diameter of $4.2^{+1.7}_{-1.0}$ kpc, the $\sqrt{\mu }d$ of our sample is one to six times the observed intrinsic diameters (1.0–2.5 kpc). Lensing (or multiplicity) increases the apparent effective size of a source, so this comparison favours a lensing (or multiplicity) interpretation for the ACT-selected sources.

2021ApJ...908..164K__Beckwith_et_al._2006_Instance_1

While these S/Ns may not look promising, there is an interesting question that one can ask: How much LUVOIR-15 m time is needed to detect the present Earth-level concentration of NO2 around a Sun-like star at 10 pc? Figure 6(a) shows the spectrum of the difference in geometric albedo with and without NO2. Also shown as dashed lines are the noise levels for 10 (blue), 300 (red), and 1200 (black) hr of LUVOIR-A observation times. The present Earth level of NO2 seems to be well above the noise level after 300 hr of observation time (compare the solid green curve with dashed red line), indicating that it might be detectable. To find out with what S/N it would be detectable, Figure 6(b) shows the “net S/N” to detect present Earth-level NO2 as a function of observation time. To achieve a net S/N of 5 (dashed red line), it would take LUVOIR-15 m about 400 hr. For comparison, to obtain the Hubble Ultra Deep Field (UDF) image, ∼400 hr of actual observation time (∼1 yr in real time) was needed (Beckwith et al. 2006). In fact, Hubble has run even larger programs, such as the CANDLES galaxy evolution survey (Grogin et al. 2011) with 902 orbits (∼900 hr of observation time assuming ∼1 hr per orbit). This took about 3 yr in real time. However, these large programs also obtained data on a huge sample size with thousands of galaxies. LUVOIR is envisaged to be 100% community-competed time, and the final report of the LUVOIR team laid out a Design Reference Mission (DRM) in which comparable allocations of time were spent on general astrophysics observations and exoplanet detection and characterization observations during the first 5 yr of the mission. So, over the course of the nominal LUVOIR mission lifetime of about 5 yr, it may be possible to take data with ∼400 hr observation time on a prime HZ planet candidate(s) within 10 pc, to potentially obtain an S/N ∼ 5 for an NO2 feature at the present Earth level on an Earth–Sun system at 10 pc. An even more interesting aspect is that we can place upper limits on the amount of NO2 available on that planet as we spend more observation time on a prime HZ candidate. This could potentially indicate the presence or absence or the level of technological civilization on that planet.

2022MNRAS.512.4136C__Pérez-Montero_&_Contini_2009_Instance_1

If we recall the tight, monotonic dependence of the position of galaxies along the SF sequence in the diagram with metallicity (as outlined in Section 3.1), we can interpret our global results of Figs 4 and 5 as a manifestation of the existence of an O/H versus N/O relation for SDSS star-forming galaxies, whose intrinsic scatter is reflected and, to some extent, translated into the observed distribution of emission line ratios within the [N ii]-BPT. A tight relationship between O/H and N/O abundances is indeed observed in both H ii regions and local galaxies, especially at M⋆ ≳ 109.5M⊙ (Vila Costas & Edmunds 1993; van Zee et al. 1998; Pérez-Montero & Contini 2009; Pilyugin et al. 2012; Andrews & Martini 2013; Hayden-Pawson et al. 2021), and it is set by the predominant nucleosynthetic origin of nitrogen from CNO burning of pre-existing stellar carbon and oxygen in low- and intermediate-mass stars experiencing the AGB phase (i.e. the ‘secondary’ nitrogen production mechanism, Kobayashi, Karakas & Umeda 2011; Ventura et al. 2013; Vincenzo et al. 2016); alternatively, Vincenzo & Kobayashi (2018) reproduced the observed N/O-O/H relation introducing failed supernovae (SNe) in massive stars within their cosmological simulations. Recently, such relationship between O/H and N/O has been suggested as even tighter than the one between M⋆ and N/O (Hayden-Pawson et al. 2021), in contrast to what claimed by previous studies (e.g. Andrews & Martini 2013; Masters et al. 2016). In light of our results, this would confirm that deviations in N/O at fixed O/H are more likely to be related to the offset from the SF sequence in the [N ii]-BPT than relative variations in M⋆, although the two are clearly physically correlated. The connection between the two diagrams is also readily evident if we look at the distribution of our galaxy sample in the N/O versus O/H diagram, as shown in Fig. 8 (where [N ii] λ6584/[O ii] λ3727, 29 is converted to N/O following the Te-based calibrations presented in Hayden-Pawson et al. 2021); here, each hexagonal bin is colour-coded by the average distance D of galaxies from the best-fitting line of the [N ii]-BPT, almost perfectly tracing the scatter around the median N/O versus O/H relation.

2020AandA...633A..34C__Martell_&_Shetrone_2013_Instance_1

Some studies use HIPPARCOS or Gaia data to determine the evolutionary status of field LiRG and show that these objects tend to accumulate close to the RGB bump, the clump, and the early-AGB (e.g. Charbonnel & Balachandran 2000; Kumar et al. 2011; Smiljanic et al. 2018; Deepak 2019), which is in agreement with open cluster studies (e.g. Delgado Mena et al. 2016). Other works report, however, that LiRG can be randomly located in the HRD (Jasniewicz et al. 1999; Smith et al. 1999; Monaco et al. 2011; Lebzelter et al. 2012; Martell & Shetrone 2013; Casey et al. 2016). The distinction is crucial to understanding the processes that may provide an explanation for the phenomenon, such as fresh Li production by internal mixing processes (Sackmann & Boothroyd 1999; Palacios et al. 2001; Guandalini et al. 2009; Strassmeier et al. 2015; Cassisi et al. 2016), prompt mass loss events (de La Reza et al. 1996, 1997), Li accretion during engulfment of planets or planetesimals (Alexander 1967; Siess & Livio 1999; Carlberg et al. 2010; Aguilera-Gómez et al. 2016a,b; Delgado Mena et al. 2016), tidal interactions between binary stars (Casey et al. 2019), or a combination of these mechanisms (Denissenkov & Weiss 2000; Denissenkov & Herwig 2004). However, since the evolution tracks of evolved stars all converge to the same area of the CMD, the definitive determination of the actual evolution status of LiRG requires asteroseismology to probe their internal structure and disentangle RGB from clump stars. As of today, very few LiRG have been observed with CoRoT and Kepler. The majority seems to be located in the core-He burning clump (Silva Aguirre et al. 2014; Bharat Kumar et al. 2018; Casey et al. 2016; Smiljanic et al. 2018; Singh et al. 2019), with the others being at the RGB bump or higher on the first ascent giant branch (Jofré et al. 2015; Casey et al. 2019). In a recent study using LAMOST spectra to derive both the Li abundance and asteroseismic classification, Casey et al. (2019) showed that ∼80% of their large sample of low-mass LiRG (2330 objects) probably have helium burning cores. They find that LiRG are more frequent at higher metallicity.

2018ApJ...868..139W__Schlickeiser_&_Jenko_2010_Instance_1

By radio continuum surveys of interstellar space and direct in situ measurements in the solar system, it is well established that for many scenarios the background magnetic fields are spatially varying. However, the above research about parallel and perpendicular diffusion only explored the uniform mean magnetic field. One can show that the spatially varying background magnetic fields lead to the adiabatic focusing effect of charged energetic particle transport and introduces correction to the particle diffusion coefficients (see, e.g., Roelof 1969; Earl 1976; Kunstmann 1979; Beeck & Wibberenz 1986; Bieber & Burger 1990; Kóta 2000; Schlickeiser & Shalchi 2008; Shalchi 2009b, 2011; Litvinenko 2012a, 2012b; Shalchi & Danos 2013; Wang & Qin 2016; Wang et al. 2017b). To explore the influence of adiabatic focusing on particle transport, the perturbation method is frequently used (see, e.g., Beeck & Wibberenz 1986; Bieber & Burger 1990; Schlickeiser & Shalchi 2008; Schlickeiser & Jenko 2010; Litvinenko & Schlickeiser 2013; He & Schlickeiser 2014). To use the perturbation method, since the anisotropic distribution function is an implicit function, by using the iteration method, one can find that the anisotropic distribution function becomes an infinite series of the spatial and temporal derivatives of the isotropic distribution function. Therefore, the governing equation of the isotropic distribution function derived from the Fokker–Planck equation contains infinite terms because of the infinite series of the anisotropic distribution function. By using the truncating method to neglect the higher-order derivative terms, the approximate correction formulas of parallel or perpendicular diffusion coefficients were obtained (see, e.g., Schlickeiser & Shalchi 2008; Schlickeiser & Jenko 2010; He & Schlickeiser 2014). However, the higher-order derivative terms probably also make the correction to the parallel and perpendicular diffusion much like the lower-order derivative ones do. The magnitude of the correction from higher-order derivative term might not necessarily be a higher-order small quantity than the magnitude of the lower-order derivative terms. Therefore, the correction obtained by the previous authors is likely to contain significant errors. In this paper, by considering the higher-order derivative terms, we derive the parallel and perpendicular diffusion coefficients and obtain the correction formulas coming from all order derivative terms by using the improved perturbation method (He & Schlickeiser 2014) and the iteration operation. And for the weak adiabatic focusing limit we evaluate the correction to the parallel diffusion coefficient and compare it with the correction obtained in the previous papers.

2018ApJ...854...26L__Tian_2017_Instance_2

The hot emission line of Fe xxi 1354.09 Å and the cool emission line of Si iv 1402.77 Å have been used in many spectroscopic studies to investigate chromospheric evaporation (e.g., Tian et al. 2014, 2015; Li et al. 2015b, 2017a, 2017b; Brosius et al. 2016; Zhang et al. 2016a, 2016b). It is widely accepted that the forbidden line of Fe xxi 1354.09 Å is a hot (log T ∼ 7.05) and broad emission line during solar flares (Doschek et al. 1975; Cheng et al. 1979; Mason et al. 1986; Innes et al. 2003a, 2003b). Meanwhile, IRIS spectroscopic observations show that Fe xxi 1354.09 Å is always blended with a number of cool and narrow emission lines, which are from neutral or singly ionized species. Those blended emission lines can be easily detected at the position of the flare ribbon, including known and unknown emission lines, such as the C i line at 1354.29 Å, the Fe ii lines at 1353.02 Å, 1354.01 Å, and 1354.75 Å, the Si ii lines at 1352.64 Å and 1353.72 Å, and the unidentified lines at 1353.32 Å and 1353.39 Å (e.g., Li et al. 2015a, 2016a; Polito et al. 2015, 2016; Tian et al. 2015, 2016; Young et al. 2015; Tian 2017). In order to extract the hot line of Fe xxi 1354.09 Å and the cool line of C i 1354.29 Å (log T ∼ 4.0; Huang et al. 2014), we apply a multi-Gaussian function superimposed on a linear background to fit the IRIS spectrum at the “O i” window (e.g., Li et al. 2015a, 2016a), which has been pre-processed (i.e., IRIS spectral image deformation, bad pixel despiking and wavelength calibration) with the standard routines in Solar Soft Ware (SSW; Freeland et al. 2000). In short, the line positions and widths of these blended emission lines are fixed or constrained, and their peak intensities are tied to isolated emission lines from similar species. More details can be found in our previous papers (Li et al. 2015a, 2016a). On the other hand, the cool line of Si iv 1402.77 Å (log T ∼ 4.8) at the “Si iv” window is relatively isolated, and it can be well fitted with a single-Gaussian function superimposed on a linear background (Li et al. 2014, 2017a). Using the relatively strong neutral lines (i.e., “O i” 1355.60 Å and “S i” 1401.51 Å), we also perform an absolute wavelength calibration for the spectra at the “O i” and “Si iv” windows, respectively (Tian et al. 2015; Tian 2017). Finally, the Doppler velocities of Fe xxi 1354.09 Å, C i 1354.29 Å, and Si iv 1402.77 Å are determined by fitting line centers removed from their rest wavelengths (Cheng & Ding 2016b; Guo et al. 2017; Li et al. 2017a). As the hot Fe xxi line is absent in the non-flaring spectrum, the rest wavelength for the Fe xxi line (i.e., 1354.09 Å) is determined by averaging the line centers of the Fe XXI profiles which were used in the previous IRIS observations (Brosius & Daw 2015; Polito et al. 2015, 2016; Sadykov et al. 2015; Tian et al. 2015; Young et al. 2015; Brosius et al. 2016; Lee et al. 2017), while the rest wavelengths for the C i and Si iv lines, i.e., 1354.29 Å and 1402.77 Å, respectively, are determined from their quiet-Sun spectra (Li et al. 2014, 2015a).

2022MNRAS.512..439C__Lian_et_al._2021_Instance_1

It is still unclear whether this incompatibility is evidence against the spatially flat ΛCDM model or is caused by unidentified systematic errors in one of the established cosmological probes or by evolution of the parameters themselves with the redshift (Dainotti et al. 2021b, 2022). Newer, alternate cosmological probes could help alleviate this issue. Recent examples of such probes include reverberation-mapped quasar (QSO) measurements that reach to redshift z ∼ 1.9 (Czerny et al. 2021; Khadka et al. 2021a,b; Yu et al. 2021; Zajaček et al. 2021), H ii starburst galaxy measurements that reach to z ∼ 2.4 (Mania & Ratra 2012; Chávez et al. 2014; González-Morán et al. 2019, 2021; Cao, Ryan & Ratra 2020, 2022a; Cao et al. 2021a; Johnson, Sangwan & Shankaranarayanan 2022; Mehrabi et al. 2022), QSO angular size measurements that reach to z ∼ 2.7 (Cao et al. 2017, 2020, 2021a; Ryan, Chen & Ratra 2019; Lian et al. 2021; Zheng et al. 2021), QSO flux measurements that reach to z ∼ 7.5 (Risaliti & Lusso 2015, 2019; Khadka & Ratra 2020a,b, 2021, 2022; Lusso et al. 2020; Yang, Banerjee & Ó Colgáin 2020; Li et al. 2021; Lian et al. 2021; Luongo et al. 2021; Rezaei, Solà Peracaula & Malekjani 2021; Zhao & Xia 2021),1 and the main subject of this paper, gamma-ray burst (GRB) measurements that reach to z ∼ 8.2 (Amati et al. 2008, 2019; Cardone, Capozziello & Dainotti 2009; Cardone et al. 2010; Samushia & Ratra 2010; Dainotti et al. 2011, 2013a,b; Postnikov et al. 2014; Wang, Dai & Liang 2015; Wang et al. 2016, 2022; Fana Dirirsa et al. 2019; Khadka & Ratra 2020c; Hu, Wang & Dai 2021; Dai et al. 2021; Demianski et al. 2021; Khadka et al. 2021c; Luongo et al. 2021; Luongo & Muccino 2021; Cao et al. 2021a). Some of these probes might eventually allow for a reliable extension of the Hubble diagram to z ∼ 3–4, well beyond the reach of Type Ia supernovae. GRBs have been detected to z ∼ 9.4 (Cucchiara et al. 2011), and might be detectable to z = 20 (Lamb & Reichart 2000), so in principle GRBs could act as a cosmological probe to higher redshifts than 8.2.

2017AandA...601A...4A__Cernicharo_et_al._1999_Instance_1

In addition to thermal excitation through collisions with H2 and He, absorption of infrared photons and pumping to excited vibrational states, followed by radiative decay to rotational levels in the ground-vibrational state, is an important excitation mechanism of molecules in IRC +10216 (Deguchi & Uyemura 1984; Agúndez & Cernicharo 2006; González-Alfonso et al. 2007; Agúndez et al. 2008, 2015; Cordiner & Millar 2009; Daniel et al. 2012; De Beck et al. 2012). Here, we have included excitation through infrared pumping for all studied species, mostly through bands lying in the mid-infrared, where the flux in IRC +10216 is large (Cernicharo et al. 1999). To facilitate the excitation and radiative transfer calculations, we have collapsed the fine rotational structure of the radicals and simply treated these species as linear molecules with a 1Σ electronic state. For C2H, we have included the first four vibrationally excited states of the bending mode (ν2 = 1, 2, 3, 4), and the first vibrationally excited states of the stretching modes (ν1 = 1 and ν3 = 1). The vibrationally excited state that plays the most important role, through infrared pumping, in the excitation of C2H in IRC +10216 is ν2 = 1, which lies 371 cm-1 above the ground-vibrational state. The wavelengths and strengths of the vibrational bands have been taken from Tarroni & Carter (2004). For the radical CN, we have included the v = 0 → 1 band, lying at 2042 cm-1 (Hübner et al. 2005; Brooke et al. 2014), which plays a minor role on the excitation of the λ 3 mm lines in IRC +10216, however. For HC3N, we have included the first excited states of the vibrational bending modes ν5 and ν6, which have strong fundamental bands at 663 and 498 cm-1. The wavelengths and strengths of the vibrational bands are from the compilation by J. Crovisier1, which are based on extensive laboratory work (e.g., Uyemura et al. 1982; Jolly et al. 2007). For cyanodiacetylene, we have included the first excited states of the vibrational bending modes ν7 and ν8, whose calculated fundamental bands, lying at 566 and 685 cm-1, have been found to be important for the rotational excitation of HC5N in IRC +10216 (Deguchi & Uyemura 1984). For the radicals C4H, C6H, and C3N there is little information on the wavelengths and strengths of vibrational bands. For these species we have instead included a generic vibrationally excited state lying at 15 μm above the ground-vibrational state, with an Einstein coefficient of spontaneous emission of 5 s-1 for the P(1) transition of the vibrational band. A similar treatment, with slightly different parameters, was adopted for C4H and C6H by Cordiner & Millar (2009).

2020AandA...640L..11B__Segretain_1996_Instance_1

Another possibly important cooling delay may arise from the phase separation of 22Ne during crystallization (Isern et al. 1991; Althaus et al. 2010). Our current best understanding is that at the small 22Ne concentrations typical of C/O white dwarfs (∼1% by number), the presence of 22Ne should not affect the phase diagram, except near the azeotropic point of the C/O/Ne phase diagram. Thus, the crystallization of the C/O core initially proceeds as in the case without 22Ne with no redistribution of neon ions between the solid and liquid phases. After a significant fraction of the core has crystallized, the temperature approaches the azeotropic point and the existing calculations indicate that the liquid phase is enriched in 22Ne relative to the solid (Segretain 1996; García-Berro et al. 2008). The 22Ne-poor solid is lighter than the surrounding liquid and floats upward where it eventually melts. This gradually displaces the 22Ne-rich liquid downward toward the solid–liquid interface until the azeotropic composition is reached, thereby releasing a considerable amount of gravitational energy. Given our very limited knowledge of the ternary C/O/Ne phase diagram (Segretain 1996; Hughto et al. 2012), this effect cannot be quantitatively implemented in our evolution models. However, we note that our current understanding of 22Ne phase separation is remarkably consistent with the missing cooling delay. In Fig. 2 we show the luminosity function obtained by adding an artificial 0.6 Gyr delay when 60% of the core is crystallized. These parameters are entirely consistent with those found in preliminary studies (Segretain 1996; García-Berro et al. 2008) and yield an excellent fit to the crystallization pile-up3. Based on the current (albeit limited) knowledge of the C/O/Ne phase diagram, we propose that the phase separation of 22Ne in the advanced stage of crystallization significantly contributes to the pile-up in the luminosity function of 0.9−1.1 M⊙ white dwarfs (Fig. 2).

2018AandA...616A..99K__Narang_et_al._2016_Instance_1

The high-resolution imaging observations of TR from IRIS reveal the ubiquitous presence of network jets. We have used three different IRIS observations of the quiet sun, which are located near the disk center. On the basis of careful inspection, 51 network jets are identified from three QS observations and used for further analysis. These 51 network jets are very well resolved and are not affected by the dynamics of other jets. The study is focused on the rotating motion of network jets along with the estimation of their other properties (speed, height, and lifetime). The mean speed, as predicted by statistical distributions of the speed, is 140.16 km s−1 with a standard deviation of 39.41 km s−1. The mean speed of network jets is very similar, as reported in previous works (e.g., Tian et al. 2014; Narang et al. 2016). However, in case of their lifetimes, we found a value that is almost double (105.49 s) that of the previously reported mean lifetime of the network jets (49.6 s; Tian et al. 2014). As mentioned above, we took only those network jets that are very well resolved in space and in the time; these criteria exclude short lifetime network jets. Therefore, our statistical distribution of the lifetime predicts a higher mean lifetime. The mean length of the network jets is 3.16 Mm with a standard deviation of 1.18 Mm. In the case of CH network jets, Tian et al. (2014) have reported that most of the network jets have lengths from 4.0 to 10.0 Mm. However, the mean length for QS network jets is smaller (3.53 Mm; Narang et al. 2016). So, the mean length for QS network jets from the present work is in good agreement with Narang et al. (2016). In addition, the apparent speed and length of these network jets are positively correlated, which is very similar to what has already been reported in previous works (Narang et al. 2016). Finally, we can say that these networks jets are very dynamic features of the solar TR, as revealed by their estimated properties.

2016AandA...589A..73R__Husser_et_al._2013_Instance_1

Single-burst stellar population (SSP) models mimic uniform stellar populations of fixed age and metallicity, and are an important tool to study unresolved stellar clusters and galaxies. They are created by populating theoretical stellar evolutionary tracks with stars of a stellar library, according to a prescription given by a chosen initial mass function (IMF). Thus, the quality of the resulting SSP models depends significantly on the completeness of the used input stellar library in terms of evolutionary phases represented by the atmospheric parameters temperature, Teff, surface gravity, log (g), and metallicity. A sufficiently large spectral coverage is equally crucial when constructing reasonable SSP models. Theoretical stellar libraries like, e.g. BaSeL (Kurucz 1992; Lejeune et al. 1997, 1998; Westera et al. 2002), or PHOENIX (Allard et al. 2012; Husser et al. 2013) are generally available for both a large range in wavelength and in stellar parameters, whereas empirical libraries are found to be more incomplete in both respects. However, the advantage of the latter ones is that they are not hampered by the still large uncertainties in the calculation of model atmospheres. Examples of empirical stellar libraries in the optical wavelenth range encompass the Pickles library (Pickles 1998), ELODIE (Prugniel & Soubiran 2001), STELIB (Le Borgne et al. 2003), Indo-US (Valdes et al. 2004), MILES (Sánchez-Blázquez et al. 2006), and CaT (Cenarro et al. 2001, 2007). In the near-infrared (NIR) and mid-infrared (MIR)1, only very few empirical libraries have been observed so far (e.g. Lançon & Wood 2000; Cushing et al. 2005; Rayner et al. 2009). The NASA Infrared Telescope Facility (IRTF) spectral library, described in the latter two papers, is to date the only empirical stellar library in the NIR and MIR which offers a sufficiently complete coverage of the stellar atmospheric parameter space to construct SSP models. In the future, the X-Shooter stellar library, which contains around 700 stars, and which covers the whole optical (see Chen et al. 2014) and NIR wavelength range until 2.5 μm, will clearly improve the current situation in the NIR.

2022AandA...659A..41E__Hobbs_et_al._2005_Instance_1

The age of a neutron star is difficult to measure, as for many other astronomical sources. The most robust way to do it is by identifying the birth supernova of the neutron star. However, this can be done precisely only for a very small number of objects, as 5–10 supernovae have historically been observed in our galaxy (Stephenson & Green 2005), and neutron stars are faint sources–practically undetectable at distances beyond the Magellanic Clouds. The explosions, however, leave imprints in the interstellar medium that can remain visible at radio wavelengths for 10 − 100 kyr (Sarbadhicary et al. 2017), thereby allowing the association of pulsars with supernova remnants (SNRs). However, pulsars are rarely found at the centre of SNRs (Frail et al. 1994), as most are expelled like bullets during the explosions possibly due to asymmetries in the process (e.g. Socrates et al. 2005). The transverse velocities of pulsars (based on proper motion and distance estimates) are particularly large, with a mean close to 310 km s−1 (Hobbs et al. 2005), which is at least ten times larger than the average velocities for stars in the solar neighbourhood (e.g. Gaia Collaboration 2018). Moreover, some measured velocities range as high as 1000 km s−1 (Chatterjee et al. 2005; Deller et al. 2019). Thus, associations between SNRs and pulsars are not always straightforward to make (e.g. see the chapter on young pulsars in Lyne & Graham-Smith 2012). The farther the pulsar is from the explosion site, the higher the possibility that the pulsar and SNR are unrelated. In order to confirm an association, it could be necessary to account for up to 100 kyr of evolution of the SNR (that we assume as the maximum possible age of a SNR), and movement across the Galaxy of the pulsar (e.g. Suzuki et al. 2021). In some situations, proper motion measurements for the pulsars can shed light on the matter. For an association to be secure, the pulsar must be moving away from where the explosion took place (usually adopted as the centre of the SNR), and the time necessary to move the pulsar to its current position must match the age of the system. If such time coincided with an independent age measurement of the SNR or the pulsar, or both, then the association would be concretely confirmed. However, this is rarely possible as SNR and pulsar ages are hard to obtain.

2019MNRAS.490.2071Y__Riess_et_al._2018_Instance_2

Set II: we now focus on the observational constraints on the model parameters after the inclusion of the local measurement of H0 by Riess et al. (2018) with the previous data sets (CMB, Pantheon, and CC) in order to see how the parameters could be improved with the inclusion of this data point. Since for this present UM, the estimation of H0 from CMB alone is compatible with the local estimation of H0 by Riess et al. (2018), thus, we can safely add both the data sets to see whether we could have something interesting. Following this, we perform another couple of tests after the inclusion of R18. The observational results on the model parameters are summarized in Table 4. However, comparing the observational constraints reported in Table 3 (without R18 data) and Table 4 (with R18), one can see that the inclusion of R18 data (Riess et al. 2018) does not seem to improve the constraints on the model parameters. In fact, the estimation of the Hubble constant H0 remains almost similar to what we found in Table 3. In order to be more elaborate in this issue, we have compared the observational constraints on the model parameters before and after the inclusion of R18 to other data sets. In Figs 7 (CMB versus CMB+R18), 8 (CMB+CC versus CMB+CC+R18), 9 (CMB+Pantheon versus CMB+Pantheon+R18), and 10 (CMB+Pantheon+CC versus CMB+Pantheon+CC+R18), we have shown the comparisons which prove our claim. One can further point out that the strong correlation between the parameters μ and H0 as observed in Fig. 5 still remains after the inclusion of R18 [see specifically the (μ, H0) planes in Figs 7–10]. The physical nature of μ does not alter at all. That means the correlation between H0 and μ is still existing after the inclusion of R18 to the previous data sets, such as CMB, Pantheon, and CC. In addition to that since μ ≲ 0.9 according to all the observational data sets, thus, the transition from past decelerating era to current accelerating era occurs to be around z ≲ 0.6, similar to what we have found with previous data sets (Table 3).

2022AandARv..30....6M__Blanton_et_al._2001_Instance_1

In a similar fashion to what done by the CARLA survey, the COBRA (Clusters Occupied by Bent Radio AGN) program (Paterno-Mahler et al. 2017) searches for overdense regions around radio-AGN with double-lobed structures which are not aligned with each other, but bent by forming angles 180∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$180^\circ$$\end{document}. The rationale behind this search is that the radio lobes of these AGNs are most likely bent because of the ram pressure that occurs due to the relative motion of the AGN host galaxy and the ICM (e.g., Feretti et al. 1992; Blanton et al. 2001; Giacintutti and Venturi 2009; Wing and Blanton 2011), which makes these sources good tracers for finding galaxy clusters. Indeed, out of 646 bent radio-AGN, 530 (corresponding to ∼\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document} 82% of the original sample) are associated with over-densities—mostly at high, z=1-3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z=1-3$$\end{document}, redshifts—in the Spitzer/IRAC maps, and 190 are associated with galaxy cluster candidates. By following up on the previous work, Golden-Marx et al. (2021) also show for a subsample of 36 high-z (0.35z2.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.35z2.2$$\end{document}) cluster candidates that radio-AGN with narrower (≲80∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lesssim 80^\circ$$\end{document}) opening angles reside in richer clusters (cf. Fig. 26), clearly indicating that the cluster environment impacts radio morphology.

2017AandA...608A...8L__Momose_et_al._(2014)_Instance_1

At high redshift, the mapping of the extended Lyα haloes around galaxies (non-AGN) is however a lot more difficult because of sensitivity and resolution limitations. Detections of extended Lyman alpha emission at high redshift have been obtained in the past. While some large Lyα blobs have been observed (e.g. Steidel et al. 2000; Matsuda et al. 2004, 2011), most of these studies were forced to employ stacking analyses because of sensitivity limitations. The first tentative detections of Lyα haloes around normal star-forming galaxies emitting Lyα emission using narrowband (NB) imaging methods were reported by Møller & Warren (1998) and Fynbo et al. (2001). Later, Hayashino et al. (2004) observed 22 Lyman break galaxies (LBG) and detected extended Lyα emission by stacking the NB images. These authors were followed six years later by Ono et al. (2010) who detected Lyα haloes in their composite NB images of 401 Lyα emitters (LAEs) at z = 5.7 and 207 at z = 6.6. Matsuda et al. (2012) and Momose et al. (2014) significantly increased the size of LAEs samples used by stacking ≈2000 and ≈4500 LAEs at redshift z ≃ 3 and 2.2 ≤ z ≤ 6.6, respectively. Momose et al. (2014) found typical Lyα halo exponential scale lengths of 5–10 physical kpc. Matsuda et al. (2012) found that Lyα halo sizes are dependent on environmental density; these halo sizes extend from 9 to up to 30 physical kpc towards overdense regions. More recently, Xue et al. (2017) studied ≈1500 galaxies in two overdense regions at z ≈ 3 and 4. Using stacking methods these authors reported Lyα halo exponential scale lengths of 5–6 physical kpc and found that Lyα halo sizes correlate with the UV continuum and Lyα luminosities, but not with overdensity. Steidel et al. (2011) stacked 92 brighter (RAB ≃ 24.5) and more massive LBGs at z = 2.3−3, finding large Lyα extents of ≈80 physical kpc beyond the mean UV continuum size at a surface brightness level of ~10-19 erg s-1 cm-2 arcsec-2. Put together, all these studies showed that Lyman alpha emission is on average more spatially extended than the UV stellar continuum emission from galaxy counterparts.

2021MNRAS.507..904N__Castor,_Abbott_&_Klein_1975_Instance_1

In equations (2) and (3), $\boldsymbol{f}_{\rm rad}=(f_{{\rm rad},\, r},\, f_{{\rm rad},\theta })$ is the radiation force described as (6)$$\begin{eqnarray*} \boldsymbol{f}_{\rm rad}=\frac{\sigma _{\rm e} \boldsymbol{F}_{\rm D}}{c}+\frac{\sigma _{\rm e} \boldsymbol{F}_{\rm line}}{c}M, \end{eqnarray*}$$where σe is the mass-scattering coefficient for free electrons, $\boldsymbol{F}_{\rm D}$ is the radiation flux emitted from the accretion disc integrated by the wavelength throughout the entire range, and $\boldsymbol{F}_{\rm line}$ is the line-driving flux, which is the same as $\boldsymbol{F}_{\rm D}$ but integrated across the UV band of 200–3200 Å. The second term of equation (6) is the line force. As mentioned in Section 1, the line transitions depend on the wavelength of the radiation. The line force is exerted mainly by the radiation flux in the UV band (200–3200  Å), because the metal lines are densely distributed (e.g. Castor, Abbott & Klein 1975; Stevens & Kallman 1990). Thus, in this paper, we evaluate the line force using the UV (200–3200  Å) radiation flux and the corresponding force multiplier M same as Proga & Kallman (2004). Here M is the numerical factor indicating how much the spectral lines enhance the radiation force compared to the Thomson scattering. The radial components of the fluxes are estimated as $F^r_{\rm D}= F^r_{\rm D,\, thin}e^{-\tau _{\rm e}}$ and $F^r_{\rm line}= F^r_{\rm line,\, thin}e^{-\tau _{\rm e}}$, respectively, where τe is the electron-scattering optical depth estimated as $\tau _{\rm e} =\int ^r_{r_{\rm in}} \sigma _{\rm e} \rho (r^{\prime },\, \theta) \mathrm{ d}r^{\prime }$, where rin is the inner boundary of the computational box. We ignore the attenuation in the θ-direction as $F^\theta _{\rm D}= F^\theta _{\rm D,\, thin}$ and $F^\theta _{\rm line}= F^\theta _{\rm line,\, thin}$. We calculate $\boldsymbol{F}_{\rm D,\, thin}$ and $\boldsymbol{F}_{\rm line,\, thin}$ by integrating intensity transferred in the optically thin media from the grids on the disc to the point of interest. Here we employ the standard disc model (Shakura & Sunyaev 1973). We divide the hot region of the disc where the effective temperature is larger than $3\times 10^3\, {\rm K}$ into the grids. In contrast to the previous method where we prepared 4096 uniform grids both in the r- and φ-directions, we here prepare 12 800 grids whose sizes are determined by Δri/Δri − 1 = 1.0005 in the r-direction. In the φ-direction, we set 1600 uniform grids in the range of 0 ≤ φ 2π. In order to resolve the hot region of the disc ($T_{\rm eff}\gt 3\times 10^3 \, {\rm K}$) for the IMBHs (MBH = 103–$10^6 \, M_\odot$), a large number of grids is required in the r-direction. This is because the size of the hot region normalized by Schwarzschild radius RS increases with the decrease of the BH mass. For example, the outer radius of the hot region is ∼1000RS for $M_{\rm BH}=10^8\, M_\odot$ while it reaches as far as 3 × 104RS for $M_{\rm BH}=10^3\, M_\odot$.

2020ApJ...901...10D__Raddi_et_al._2015_Instance_1

In order to calculate oxygen fugacities, we follow the methods described by Doyle et al. (2019). From the element abundance ratios, we assign oxygen to Mg, Si, Ca, and Al in the necessary proportions to obtain the relative abundances of the charge-balanced rock-forming oxide components MgO, SiO2, CaO, and Al2O3. The remaining excess oxygen ( ), is assigned to Fe to make FeO until either O or Fe is exhausted. The excess oxygen available to make FeO is obtained using 4 where is the amount of oxygen needed to form the metal oxide, i, and OTotal is the total abundance of oxygen in the system. Other studies have used similar methods for budgeting oxygen (Klein et al. 2010, 2011; Farihi et al. 2011, 2013, 2016; Raddi et al. 2015). Once the relative abundances of the oxides are obtained, they are normalized to 1, yielding mole fractions and permitting application of Equation (3). In principle, if insufficient oxygen exists to pair with Fe to make FeO, then the Fe that remains should have been present as a metal in the accreted parent body. We emphasize that oxygen fugacity is recorded by the mole fraction of FeO, which depends on all of the oxides (FeO, SiO2, MgO, , CaO) and not simply the FeO/Fe ratio for the body. It is possible for metal and water to have coexisted in the parent body if it was undifferentiated, meaning that oxygen which is attributed to FeO in this calculation may have existed as H2O in the parent body. However, during the differentiation of a rocky body, the oxygen from ices will oxidize metallic Fe to form FeO. We are assuming here that the bodies we are observing in the WDs were either differentiated themselves, or they are the building blocks of differentiated bodies (chondrite meteorites would be the appropriate analog). Where a parent body was composed in part of Fe metal and H2O, our calculation is a measure of the prospects for FeO, and thus the ΔIW expected for the body taken as a whole, including accreted rock and ices.

2022AandA...666A.153D__Dartois_2005_Instance_1

In the first stages of star formation, protostars are still embedded in their parental cloud, where an active gas-grain chemistry is at work. Using either (i) background stars for dense clouds or (ii) a nascent protostellar object once it is able to emit sufficient light flux in the vibrational infrared wavelength range, or in a few protoplanetary disks well inclined towards the observer, the infrared pencil beam allows the probing of the composition of the cloud or circumstellar dust. The low-temperature ice mantles formed on top of or mixed with refractory dust (silicates and/or organics) can be retrieved. A harvest of astronomical observations from ground-based telescopes (e.g. UKIRT, IRTF, CFHT, and VLT) or satellites (e.g. IRAS, ISO, Akari, and Spitzer) of such lines of sight has led, since the late seventies, to the deciphering of the chemical compositions, column densities, and variations associated with these ice mantles (e.g. Boogert et al. 2008, 2015; Öberg et al. 2011; Dartois 2005; van Dishoeck 2004; Gibb et al. 2004; Keane et al. 2001; Dartois et al. 1999b; Brooke et al. 1999, and references therein). The interpretation of these observed spectra is mainly based on their comparison with the infrared spectra of laboratory-produced ice films of well-controlled composition and cryogenic temperatures (e.g. Hudson et al. 2014, 2021; Palumbo et al. 2020; Rachid et al. 2020; Terwisscha van Scheltinga et al. 2018; Öberg et al. 2007; Dartois et al. 2003, 1999a,b; Moore & Hudson 1998; Ehrenfreund et al. 1997; Gerakines et al. 1995; Hudgins et al. 1993). The routes investigated are the influence of the ice mixture on the line width and position, temperature modifications, segregation (phase separation), and/or intermolecular interactions (polar or apolar ices and molecular complexes). The impact of a distribution of grain shapes, mainly in the Rayleigh regime, is also explored in some cases. The literature is dominated by analyses based on the decomposition of the observed astronomical profiles into principal components from different ice mixtures. When dust grains evolve from the diffuse interstellar medium (ISM) to the dense phase and the protoplanetary phases, grains grow. This will affect the observed profiles and is expected to be, at least partly, responsible for enhanced scattering effects in dense cloud evolution, often referred to as cloudshine or coreshine effects (Ysard et al. 2016, 2018; Saajasto et al. 2018; Jones et al. 2016; Steinacker et al. 2015; Lefèvre et al. 2014). The growth can also be inferred from the evolution of the silicate-to-K band ratio (τ9,7/AK; e.g. Madden et al. 2022; van Breemen et al. 2011; Chiar et al. 2007).

2021ApJ...919...33C__Staubert_et_al._2019_Instance_1

The variability of the cyclotron line centroid energy in the spectra of XRPs is considered to be related to the geometry of accretion flow in close proximity to the surface of an NS. The geometry of the emitting region is related to the mass accretion rate. At low mass accretion rates, the radiation pressure is small, and one expects hot spots at the NS surface. At high mass accretion rates, the radiation pressure is high enough to stop accreting material above the NS surface. In this case, the accretion column supported by radiation pressure and confined by a strong magnetic field arises above the stellar surface (Basko & Sunyaev 1976; Wang & Frank 1981; Mushtukov et al. 2015a). The luminosity separating these two accretion regimes is called the “critical” luminosity Lcrit. The critical luminosity was shown to be dependent on the magnetic field strength (Mushtukov et al. 2015b). The dynamics of the cyclotron line was shown to be dependent on the luminosity state of XRPs (see Staubert et al. 2019 for review). In particular, the positive correlation between the cyclotron line centroid energy and accretion luminosity is considered to be typical for subcritical XRPs (Staubert et al. 2007; Klochkov et al. 2012; Fürst et al. 2014), while the negative correlation was detected in bright supercritical sources (Mihara et al. 2004; Tsygankov et al. 2006; Boldin et al. 2013). At the same time, there are some sources without any observed correlation between the cyclotron line energy and luminosity (Caballero et al. 2007). Several theoretical models are aiming to explain the variability of a cyclotron line. The positive correlation was explained by the Doppler effect in the accretion channel (Mushtukov et al. 2015c) and alternatively by the onset of collisionless shock above hot spots at low mass accretion rates (Shapiro & Salpeter 1975; Rothschild et al. 2017). The negative correlation was explained by the variations of accretion column height above the NS surface. Different models consider different locations of a line-forming region at supercritical mass accretion rates, which might be a radiation-dominated shock on top of an accretion column (Becker et al. 2012) or NS surface (see e.g., Poutanen et al. 2013; Lutovinov et al. 2015; Mushtukov et al. 2018), which reprocesses a large fraction of beamed radiation from the accretion column. Alternatively, Nishimura (2014) argued that some variations of cyclotron line centroid energy could be related to the changes of a beam pattern. By considering the structure of an accretion column in two dimensions, Nishimura (2019) suggested that the line-forming region is a region around an accretion mound in which the bulk velocity in the line-forming region can be considerably different from that in the continuum-forming region, which is assumed to be inside an accretion mound, so that the variation of Ecyc results from the motion of the accretion mound in the different luminosity ranges.

2022MNRAS.512.1499R__LeVeque_1992_Instance_1

Let ui be the evolved quantity at the coordinate position xi. Then, THC_M1 approximates the derivative of the flux f(u) at the location xi as (31)$$\begin{eqnarray} \partial _x f (u) \simeq \frac{F_{i - 1 / 2} - F_{i + 1 / 2}}{\Delta x}, \end{eqnarray}$$where Fi − 1/2 and Fi + 1/2 are numerical fluxes defined at $x_i \mp \frac{\Delta x}{2}$, respectively. The fluxes are constructed as linear combination of a non-diffusive second order flux $F^{\operatorname{HO}}$ and a diffusive first order correction $F^{\operatorname{LO}}$: (32)$$\begin{eqnarray} F_{i + 1 / 2} = F_{i + 1 / 2}^{\operatorname{HO}} - A_{i + 1 / 2} \varphi _{i + 1 / 2} \left(F_{i + 1 / 2}^{\operatorname{HO}} - F_{i + 1 / 2}^{\operatorname{LO}}\right) . \end{eqnarray}$$The term φi + 1/2 is the so-called flux limiter (LeVeque 1992), while Ai + 1/2 is a coefficient introduced to switch off the diffusive correction at high optical depth (more below). The role of the flux limiter is to introduce numerical dissipation in the presence of unresolved features in the solution u and ensure the non-linear stability of the scheme. In particular, if Ai + 1/2φi + 1/2 = 0 the second-order flux is used, while if Ai + 1/2φi + 1/2 = 1, then the low order flux is used. A standard second order non-diffusive flux is used for $F^{\operatorname{HO}}$, while the Lax–Friedrichs flux is used for $F^{\operatorname{LO}}$: (33)$$\begin{eqnarray} F^{\operatorname{HO}}_{i + 1 / 2} = \frac{f (u_i) + f (u_{i + 1})}{2}, \end{eqnarray}$$(34)$$\begin{eqnarray} F^{\operatorname{LO}}_{i + 1 / 2} = \frac{1}{2} [f (u_i) + f (u_{i + 1})] - \frac{c_{i + 1 / 2}}{2} [u_{i + 1} - u_i] . \end{eqnarray}$$The characteristic speed in the Lax–Friedrichs flux ci is taken to be the maximum value of the speed of light between the right and left cells (35)$$\begin{eqnarray} c_{i + 1 / 2} = \max _{a \in \lbrace i, i + 1 \rbrace } \left\lbrace \left| \alpha _a \sqrt{\gamma _a^{x x}} \pm \beta _a^x \right| \right\rbrace . \end{eqnarray}$$We remark that it is known that the M1 system can, in some circumstances, lead to acausal (faster than light) propagation of neutrinos in GR (Shibata et al. 2011). For this reason, one might argue that a better choice of the characteristic velocity for the Lax–Friedrichs formula would have been given by the eigenvalue of the Jacobian of $\boldsymbol{F}$. These values are known analytically (Shibata et al. 2011), however in our preliminary tests we found that the use of the full eigenvalues resulted did not improve on the stability or accuracy of the M1 solver.

2020MNRAS.494.2948P__Lyne_et_al._1990_Instance_1

Spider pulsar systems are characterized by having a low-mass companion star in a compact orbit with an energetic millisecond pulsar (MSP) resulting in heavy irradiation of the companion by the pulsar’s wind. The spider pulsar population has been observed to have a clearly bimodal distribution of companion star masses (Roberts 2011; Strader et al. 2019) made up of two distinct subgroups: black widows (BW) with companion star masses ∼0.01–0.05 M⊙, and redbacks (RB) with companion star masses ∼0.1–1 M⊙. A large proportion of the spiders, whether BWs or RBs, have been observed to exhibit (quasi-)periodic eclipses of the pulsars’ radio emission (e.g. Fruchter, Stinebring & Taylor 1988; Lyne et al. 1990) that are generally attributed to excess material in the orbits – that has been driven from the companion stars by the pulsar irradiation (Podsiadlowski 1991; van den Heuvel & van Paradijs 1988; Phinney et al. 1988; Kluzniak et al. 1988) – interfering with the propagation of the radio emission. Studies of such eclipses are key for understanding mass loss from the irradiated companion stars, the properties of the medium causing the eclipses, interactions between the pulsar wind and the eclipse medium, and the mechanisms responsible for the apparent attenuation of pulsar radio emission during the eclipse. In the years after the initial BW discovery (Fruchter et al. 1988), there were a number of excellent early works (e.g. Ryba & Taylor 1991; Stappers et al. 2001a) investigating the observed radio eclipses. However, unfortunately, a lack of further in-depth eclipse analyses – largely as a result of difficult observing requirements and (until recently) a low number of known spider pulsars – has meant slow progress in reaching an understanding in any of these topics. However, the last few years have marked a revival of the field with detailed and novel studies beginning to give important insight into the nature of eclipsing pulsar systems (e.g. Broderick et al. 2016; Main et al. 2018; Li et al. 2019).

2016ApJ...833...76B__Klimchuk_et_al._2008_Instance_2

A significant limitation of the model is that it ignores the well-established hydrodynamic evolution of the loop during the cooling process, involving the substantial transfer of mass between the chromosphere and the corona. For large downward heat fluxes, the transition region is unable to radiate the supplied energy, resulting in the deposition of thermal energy in the dense chromosphere. The resulting two to three orders-of-magnitude temperature enhancements create a large pressure gradient that drives an upward enthalpy flux of “evaporating” plasma. However, as the loop cools, the decreased heat flux becomes insufficient to sustain the radiation emitted in the now-dense transition region and hence an inverse process of downward enthalpy flux starts to occur. It has been suggested (Klimchuk et al. 2008) that the enthalpy fluxes associated with both evaporating and condensing plasma are at all times in approximate balance with the excess or deficit of the heat flux relative to the transition region radiation loss rate. This basic idea has allowed the development of global “Enthalpy-Based Thermal Evolution of Loops” (EBTEL) models that describe the evolution of the average temperature and density in the coronal part of the loops; these models are generally in good agreement with one-dimensional hydrodynamic simulations (Klimchuk et al. 2008; Cargill et al. 2012a, 2012b). It is, in principle, possible to include the effects of a turbulence-controlled heat flux in EBTEL (or 1D hydrodynamic) models. If this heat flux is reduced sufficiently relative to its collisional value, then, for the reasons explained above, there will be a significant impact on the thermal evolution of the loop. Doing so, however, would still require a numerical treatment, which is beyond the scope of the present work (but which it is our intention to carry out in a future work). Instead, we adopt a simpler approach that allows a systematic and fairly transparent quantitative analysis of the impact of turbulence on the thermodynamics of post-flare loops.