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SAO/NASA Astrophysics Data System 13

2017MNRAS.470..755H__Cox_et_al._2008_Instance_1

Supermassive black holes (SMBHs) are believed to exist in the centres of all massive galaxies (Kormendy & Richstone 1995). A small proportion of these are growing, with gas accretion rates ranging from ∼10−4 to 10 M⊙ yr−1 and a proportionately wide range of bolometric luminosities (∼1042–1047 erg s−1). These are active galactic nuclei (AGNs) and may accrete large fractions of their mass in bursts of rapid accretion (Croton et al. 2006), requiring rapid inflow of gas from galaxy length-scales. Stripping the gas of enough angular momentum to allow for such rapid accretion, thereby powering the most luminous AGN, proves extremely challenging. Theoretical work suggests major mergers can provide the torque to displace such an overwhelming fraction of the angular momentum of the gas, allowing for the highest accretion rates on to the central black hole whilst transforming the galaxy morphology (Toomre & Toomre 1972; Barnes 1988; BarnesBarnes & Hernquist 1991; Di Matteo, Springel & Hernquist 2005; Cox et al. 2008). Gas rich mergers may trigger nuclear and global starbursts (Mihos & Hernquist 1994, 1996; Hopkins et al. 2006) and major mergers disrupt the morphologies of the colliding galaxies, often exhibiting long tidal tails or shells of expelled gas and stars soon after the merger has begun. Detecting this can be challenging however, since the single new galaxy has a relaxation time-scale after which morphological features of mergers fade (Tinsley 1978; Kennicutt et al. 1987; Ellison et al. 2013). Observational evidence suggesting a link between major mergers and SMBH accretion has been mixed (e.g. Gabor et al. 2009; Cisternas et al. 2011; Schawinski et al. 2011; Kocevski et al. 2012; Treister et al. 2012; Ellison et al. 2013; Villforth et al. 2014; Kocevski et al. 2015; Villforth et al. 2017). Alternatively, AGNs may be triggered secularly through, for example, disc instabilities (Bournaud et al. 2011), bars (Knapen, Shlosman & Peletier 2000; Oh, Oh & Yi 2012) or otherwise by minor mergers (Kaviraj 2013). It remains unclear whether alternatives to major merger triggering can drive several M⊙ yr−1 of gas to the central SMBHs, as is necessary to power the most luminous AGN.

2018MNRAS.473.1633K__Fremling_et_al._2014_Instance_1

Kochanek (2009) examined the statistical properties expected for surviving binary companions to SNe assuming passively evolving systems (i.e. no binary interactions). As already noted, the companions are generally significantly fainter than the exploding star, although this is frequently not the case for stripped SN progenitors – for Type Ibc SNe, it should not be surprising to find that the binary companion is more visually luminous than the SN progenitor. This point is of considerable importance for the one candidate Type Ib progenitor iPTF13bvn (Cao et al. 2013, Groh, Georgy & Ekström 2013, Bersten et al. 2014, Fremling et al. 2014, Eldridge et al. 2015, Eldridge & Maund 2016, Folatelli et al. 2016). If the initial binary fraction is F, then the fraction of passively evolving binaries that are in stellar binaries at death is (1) \begin{eqnarray} f_{\rm b} = { F \over 1 + F f_q } \quad {\rm where}\quad f_q = \int _{q_{{\rm min}}}^{q_{{\rm max}}} q^{x-1} P(q) {\rm d}q, \end{eqnarray} x ≃ 2.35 is the slope of the initial mass function (IMF), qmin ≤ q = M2/M1 ≤ qmax ≤ 1 is the mass ratio and P(q) with ∫dqP(q) ≡ 1 is the distribution of mass ratios. For a Salpeter IMF and a flat P(q) distribution extending over 0 ≤ q ≤ 1, fq = 0.426 and the fraction of SNe in stellar binaries at death is 23 per cent, 41 per cent, 57 per cent and 70 per cent for initial binary fractions of F = 25 per cent, 50 per cent, 75 per cent and 100 per cent, respectively. Such a flat f(q) distribution is commonly found for massive star binaries, although there is evidence that the widest binaries show a different distribution (e.g. Sana et al. 2012, Kobulnicky et al. 2014, Moe & Di Stefano 2016). Essentially, only the explosions of primaries occur in stellar binaries, so the fraction of SNe in stellar binaries is less than the initial fraction of binaries because some of the SNe are the explosions of secondaries. Binary evolution, particularly stellar mergers then adds further complications, but changes the rough statistics little (e.g. Sana et al. 2012).

2022MNRAS.509..693R__Foreman-Mackey_et_al._2013_Instance_1

We detect 1.33 mm continuum extended emission originating from HD 36546 (see Fig. 3). Peak and integrated flux, size, and inclination are reported in Table 2. Once deconvolved from the beam, the major axis of the disc spans 180 au, compatible with the size reported from scattered light observations (semimajor axis of 85 au, Currie et al. 2017). The inclination is directly provided by the casa tool, as obtained from the ratio of the major and minor axis deconvolved from the beam. The total mass can be estimated by assuming an optically thin dust disc as $M_d = \frac{F_{\nu } d^2}{B_{\nu }(T_{d,c}) \kappa _\nu }$, where Fν is the measured flux at 1.33 mm, d is the distance to the source, Bν is the Planck function at the corresponding dust temperature Td, c, and κν is the mass absorption coefficient that we take as κν = 2 cm2 g−1 following Nilsson et al. (2010). In order to estimate a temperature for the dust, we have fitted a modified blackbody to the available photometry at wavelengths longer than 10 µm (AKARI, WISE, IRAS, Herschel)3 and the new ALMA photometry using the emcee Affine Invariant Markov chain Monte Carlo Ensemble sampler implementation (Foreman-Mackey et al. 2013). This wavelength range was chosen to avoid the silicate emission observed in the mid-IR spectra reported by Lisse et al. (2017) around ∼10 µm. Additionally, there is some discrepancy between the photometric data of WISE, Herschel, and ALMA, and those of IRAS and AKARI. Given their larger point spread functions and lower sensitivites, we decided to exclude the latter two from the fitting. The modified blackbody model assumes all dust grains have the same composition and size, and accounts for changes in the dust emission efficiency (Qλ) via two additional free parameters: β and a reference wavelength λ0, such that the blackbody emission is modified by a factor Qλ = 1 − exp [(λ0/λ)β] (e.g. Williams et al. 2004) Therefore, our model has four free parameters: a scaling factor that controls the disc luminosity, the dust temperature, β, and λ0. Uniform priors were used for all parameters within reasonable ranges: scaling values that produce disc luminosities consistent with the observed photometry, dust temperatures between 20 and 250 K, β values between 0 and 1.5, and λ0 between 0.3 and 300 µm (the latter was explored in log scale). This analysis yields a dust temperature value of 153 ± 3 K, and a β value of 0.24 $^{+0.07}_{-0.05}$ as shown in Fig. 1. λ0 is unconstrained. Adopting this temperature value results in a dust mass of (9.0 ± 1.0) × 10−2 M⊕. Additionally, the resulting models yield Ldisc/L* = (4.43 ± 0.15) × 10−3, compatible with previous results (see Table 1).

2016AandA...591L...7B__Kruit_(1994)_Instance_1

We created 10 000 3D models of galaxies, each with an exponential disc plus a Sérsic bulge. We adopted the following functional form for the exponential disc (López-Corredoira et al. 2002): (1)\begin{equation} \label{eq:Corredoira} \rho(R,z)=\rho_{\mathrm{0}}\cdot\exp{\Bigg(\frac{-R}{h_{R}}\Bigg)}\cdot \exp{\Bigg(\frac{-|z|}{h_{z}(R)}\Bigg)}\cdot \frac{h_{z}(R)}{h_{z}(0)} \cdot \end{equation}ρ(R,z)=ρ0·exp−RhR·exp−|z|hz(R)·hz(R)hz(0)·Following the observations, we explored the effect of a linear increase in the vertical scale height, as follows: (2)\begin{equation} \label{eq:Flares_linear} h_{z}(R) = \begin{cases} h_{z}(0) & \text{if } R \leq R_{\mathrm{flare}} \\ h_{z}(0) + \dhzdR \cdot R & \text{if } R > R_{\mathrm{flare}}. \end{cases} \end{equation}hz(R)=ifR≤RflareifR>Rflare.Graham (2001) analysed a sample of 86 face-on disc-dominated galaxies previously selected by de Jong & van der Kruit (1994). This author performed a bulge + disc decomposition for 69 galaxies in the I-band, correcting for the effects of the internal extinction, Galactic extinction, inclination, and cosmological dimming (Graham 2003), that we used as a reference for our models. We estimated stellar masses using the relationship between the V-band mass-to-light ratio of galaxies and their dust-corrected rest-frame colours derived by Wilkins et al. (2013). According to these authors, for z 0.1 the optimal observed colour is (B − V), so we have estimated this colour for the 69 galaxies of Graham (2003) from HyperLeda data1, and estimated the stellar mass of each galaxy using the relations in Wilkins et al. For those objects without (B − V) available in HyperLeda, we estimated them from their SDSS (g − r) colour following the transformations published in Jester et al. (2005). To simulate realistic images of the disc galaxies, we adopted the observational I-band distributions of Graham (2001, 2003) for the photometric parameters (re, Sérsic index n, hR, μ0, μe, B/T, the absolute magnitudes of the disc Mabs,disc and the bulge Mabs,bulge), and four morphological type bins (S0–Sa, Sb–Sbc, Sc–Scd and Sd–Sdm), in three mass bins (10 log 10M/M⊙ 10.7,10.7 log 10M/M⊙ 11 and log 10M/M⊙> 11) in order to explore realistic mass distributions for the morphological type bins. In Fig. 1 we represent the distributions of the structural and photometric parameters from which we created the models and compared them to the observations they are based on. For each morphological type bin we randomly chose the ratio of scale height to scale length (hz/hR) from the observational range of values corresponding to each type reported by Kregel et al. (2002) and Mosenkov et al. (2015) in the I-band, as shown in Fig. 1.

2017MNRAS.464..183N__Biviano_&_Katgert_2004_Instance_1

Other important result we reported in Section 3.3 is the reversing behaviour of red and blue galaxies with respect to velocity and groupcentric distances segregation, with redshift. Regarding velocity segregation, the preceding paragraph provides a qualitative scenario. Now, to explain the spatial segregation, we should notice that our analyses in Sections 3.2 and 3.3 take into account galaxies within 2R/R200. One can reasonably assume that such objects at lower redshifts correspond to a mixture of descendants of galaxies at higher redshifts in the same radii and of infalling objects from outer radii. Thus, both survival and replenishment of galaxies should be expected over the time, and two important factors come into play: (i) the accretion rate of galaxies; and (ii) the orbital dependence of galaxy properties (e.g. Biviano & Katgert 2004; Iannuzzi & Dolag 2012). Indeed, regarding velocity segregation, it has also been interpreted as red and blue galaxies having different kinds of orbits, with the orbits of blue galaxies being more anisotropic than the red ones (e.g. Biviano & Katgert 2004). Recently, Biviano et al. (2016) verified that the anisotropy profile of z ∼ 1 clusters is nearly isotropic near the cluster centre, and increasingly elongated with radius. This result is consistent with a halo evolution through an initial phase of fast collapse and a subsequent slow phase of inside-out growth by accrection of field material (e.g. Lapi & Cavaliere 2009). Since the accretion rate of galaxies from the field is higher at higher redshifts (e.g. McGee et al. 2009), our sample at z ∼ 0.8 is expected to be more affected by recent infalls, which had less time to go deeper into the group potential. This could explain the development of a more marked difference between the mean groupcentric distance of red and blue galaxies (see Fig. 12). After ∼3 Gyr, part of these infalling galaxies may reach the R 2R200 region, at z ∼ 0.4, mixing with virialized and backsplash objects, and thus presenting a less pronounced radial segregation between red and blue galaxies.

2021ApJ...914L..19Z__McKernan_et_al._2012_Instance_1

Massive stars are believed to exist in the accretion disks of active galactic nuclei (AGNs). Such AGN stars and compact objects can be either the result of in situ formation inside the accretion disk or be captured from the nuclear star clusters around the AGNs (e.g., Artymowicz et al. 1993; Collin & Zahn 1999; Goodman 2003; Goodman & Tan 2004; Wang et al. 2011, 2012; Fabj et al. 2020; Cantiello et al. 2021). These AGN stars will end up with supernovae (SNe), which can eject heavy elements into the disk; this offers a possible explanation for the observational features of high-metallicity environments in AGN disks (e.g., Artymowicz et al. 1993; Hamann & Ferland 1999; Warner et al. 2003). Some compact objects, including white dwarfs (WDs), neutron stars (NSs), and black holes (BHs), can be thus formed within AGN disks. These compact objects can also be captured from the surrounding nuclear star clusters. The disk of an AGN provides a natural environment for stars and compact objects to accrete materials and to migrate within it (e.g., McKernan et al. 2012; Yang et al. 2020; Dittmann et al. 2021; Jermyn et al. 2021; Wang et al. 2021; Tagawa et al. 2021; Kimura et al. 2021). Some of these stars can be very massive and have high spin caused by accretion (Dittmann et al. 2021; Jermyn et al. 2021), so that they can easily produce high-spin stellar remnants. Abundant compact objects, especially with the presence of the massive BHs (M > 10M⊙), would likely accrete, collide, and merge within the trapping orbits, and hence would grow into ∼100 M⊙ intermediate-mass BHs (McKernan et al. 2012; Secunda et al. 2019; Yang et al. 2019b). Some AGN stars can be tidally disrupted by these BHs that can power micro-tidal disruption events (Yang et al. 2021). The death of high-spin stars and neutron star mergers are expected to power gamma-ray burst (GRB) jets, which would be always choked by the dense atmosphere of the disks (Zhu et al. 2021a, 2021b; Perna et al. 2021a). Zhu et al. (2021a) suggested that these choked jets can produce high-energy neutrinos that may contribute a substantial fraction of the diffuse neutrino background. A candidate electromagnetic (EM) counterpart that emerged from an AGN, explained as ram pressure stripping of gas within the kicked BH hill sphere colliding with the AGN disk gas (McKernan et al. 2019), was reported by the Zwicky Transient Facility (Graham et al. 2020). This was thought to be associated with a (85 + 66)M⊙ binary BH merger (GW190521) detected by the LIGO/Virgo collaboration (Abbott et al. 2020). This connection provided plausible evidence of a potentially important AGN channel for compact star mergers.

2015MNRAS.453.3414A__the_1999_Instance_1

Filippenko & Chornock (2001) first presented the dynamical estimate of mass of the source to be around 7.4 ± 1.1 M⊙. Recently, Radhika & Nandi (2014) claimed that the mass of XTE J1859+226 is perhaps in between 6.58 and 8.84 M⊙ which is similar to the prediction of Shaposhnikov & Titarchuk (2009), although the lower mass limit is estimated as 5.4 M⊙ by Corral-Santana et al. (2011). However, we consider the typical mass of the source as 7 M⊙. The distance of this source is around d ∼ 11 kpc (Filippenko & Chornock 2001). Steiner et al. (2013) measured the spin as ak ∼ 0.4; however, Motta et al. (2014b) recently reported that the spin of the source is ak ∼ 0.34. Since the spin predictions are quite close, we use ak ∼ 0.4 for this analysis. We estimate the fluxes Fx (see Table 1) of LHS and HIMS of the 1999 outburst of the source (Radhika & Nandi 2014). The corresponding disc luminosities are calculated as $L_{\rm disc}^{{\rm LHS}}=8.26 \times 10^{37}\ {\rm erg\ s^{-1}}$ and $L_{\rm disc}^{{\rm HIMS}}=1.85 \times 10^{38}\ {\rm erg\ s^{-1}}$, respectively. Now, it is reasonable to assume the accretion efficiency for rotating BH as η = 0.3 which corresponds to the accretion rate of the inflowing matter as ${\dot{M}}_{{\rm acc}}^{{\rm LHS}} = 0.304 {\dot{M}}_{{\rm Edd}}$ in LHS and ${\dot{M}}_{{\rm acc}}^{{\rm HIMS}} = 0.680 {\dot{M}}_{{\rm Edd}}$ in HIMS. For LHS, we use $R_{\dot{m}}=9.83$ per cent following our theoretical estimate where xs = 64.6rg for ak = 0.4, ${\mathcal {E}}=0.001\,98$ and λ = 3.18. Incorporating these inputs in equation (15), we obtain the jet kinetic power as $L^{{\rm LHS}}_{{\rm jet}} = 2.52\times 10^{37}\ {\rm erg\ s^{-1}}$. The maximum mass outflow rate for HIMS corresponding to ak = 0.4 is obtained from Fig. 9 as $R^{\rm max}_{\dot{m}}=17.5$ per cent for ${\mathcal {E}}=0.005\,47$ and λ = 3.1, where the shock transition occurs at 21.9rg. Using these values in equation (15), we obtain the maximum jet kinetic power as $L^{{\rm HIMS}}_{{\rm jet}} = 1.08\times 10^{38}\ {\rm erg\ s^{-1}}$ which we regard to be associated with the HIMS of this source.

2017AandA...607A..71G__Hansen_&_Oh_(2006)_Instance_1

An implication of the respective escape fractions of the two regimes is visible in Fig. 12. Here we show several values of NHI,cl for the static setup using τd,cl = 10-4 (empty symbols) and τd,cl = 1 (filled symbols), which correspond to metallicities of \hbox{$Z/Z_\odot = 0.63\left(\tau_{\rm d}/10^{-4}\right)\left(10^{17}\cm^{-2}/N_{\HI,\cl}\right)$}Z/Z⊙=0.63τd(/10-4)(1017 cm-2/NHI,cl) (Pei 1992; Laursen et al. 2009); this reaches clearly unrealistic values. However, as in this paper we are interested in the fundamental impact of the individual parameters, we also study these extreme values. Also shown in Fig. 12 (with a black [gray] solid line for the low [high] dust content) is the proposed analytic solution for fesc by Hansen & Oh (2006)(18)\begin{equation} f_{\rm esc}^{\rm HO06} = 1/{\rm cosh}(\!\sqrt{2 N_{\cl}\epsilon}), \label{eq:fescHO06} \end{equation}fescHO06=1/cosh(2Nclϵ),where for Ncl we used Eq. (8)(with (a1,b1) = (3/2, 2) as found in Sect. 4.1) and for the clump albedo (i.e., the fraction of incoming photons that are reflected) ϵ, we adopted a value of c1(1−e− τd,cl) with c1 = 1.6 [c1 = 0.06] to match the NHI,cl = 1022 cm-2 data points for τd,cl = 10-4 [τd,cl = 1]. The behavior for the low- and high-dust contents is quite different. On the one hand, the escape fractions versus NHI,cl scales for τd,cl = 1 (filled symbols in Fig. 12) as predicted by Hansen & Oh (2006) in their “surface scatter” approximation, that is, a larger clump hydrogen column density “shields” the dust better from the Lyα photons and thus prevents their destruction more efficiently. On the other hand, however, this is not the case for the low-dust scenario presented in Fig. 12 (with unfilled symbols) where a larger value of NHI,cl implies a lower fesc. This is because here the dust optical depth through all the clumps (shown in the black dotted line in Fig. 12) is lower than the accumulated dust optical depth through the subsequent random-walk clump encounters (black solid line), i.e., \hbox{$\exp(-4/3 \fc \tau_{\rm d, cl}) \lesssim f_{\rm esc}^{\rm HO06}$}exp(−4/3fcτd,cl)≲fescHO06. Consequently, configurations in the “free-streaming” regime can possess enhanced Lyα escape fractions compared to the “random walk” regime (see Sect. 5.2 for a more detailed discussion). Still, both cases possess (much) larger escape fractions than a homogeneous slab, which is shown in Fig. 12 with a black dashed line. Here, we use the derived escape fraction by Neufeld (1990) with NHI = 4/3 × fc1022 cm-2 and τd = 4/3fcτd,cl, i.e., with equal column densities as in the NHI,cl = 1022 cm-2 case.

2020MNRAS.498.5299M__Bernardeau,_Waerbeke_&_Mellier_2003_Instance_1

From the early days of detection the weak lensing (Munshi et al. 2008) studies have matured to a point where weak lensing results from Euclid are expected to constrain the cosmological parameters to sub-per cent accuracy. However, weak lensing at smaller angular scales probes the non-linear regime of gravitational clustering, and is thus key to understanding the non-Gaussianity induced by the non-linearity and fullly exploiting in the weak lensing maps. The higher order statistics are also useful for the breaking of parameter degeneracies in studies involving the power spectrum analysis alone and they are also important in understanding the variance or error of estimation of lower order statistics. These higher order statistics including the cumulants (Bernardeau 1994b) and their correlators (Bernardeau 1996a; Calabrese et al. 2010; Munshi et al. 2011; Riquelme & Spergel 2012) are among the best-known diagnostics of the deviation from Gaussianity that characterizes the non-linear regime (Bartolo et al. 2004), with a long history analytical modelling (Bernardeau et al. 2002a). Most of these studies use extensions of perturbative results in the quasi-linear regime valid at large smoothing angular scales or variants of halo models (Cooray & Sheth 2002). Early studies concentrated on measurements of higher order correlation hierarchy in the angular space due to small survey size (Bernardeau, Mellier & van Waerbeke 2002b; Bernardeau, Waerbeke & Mellier 2003). However, the near all-sky coverage of future surveys such as Euclid will let us estimate higher order statistics in the harmonic domain with unprecedented accuracy (Amendola et al. 2013). While measurements of real space correlations are much simpler in the presence of complicated survey design, the measurements for different angular scales can be highly correlated (Munshi 2000; Munshi & Jain 2000). In comparison measurements in the harmonic domain are less correlated and each mode contains (nearly) independent information in the limit of all-sky coverage. The primary motivation of this study is to develop analytical predictions for one such statistic called the skew-spectrum, and test them against numerical simulations. We will borrow the concepts developed for constructing skew-spectra for the study of non-Gaussianity in the context of CMBR observations (Planck Collaboration XVII2016b). However, we also include gravity-induced secondary non-Gaussianity. The skew-spectrum is the lowest order member in the family of higher order spectra (Munshi et al. 2011a, 2020). In a series of papers, the one-point statistics such as the skewness and kurtosis were generalized to two-point cumulant correlator, e.g. the two-to-one correlator and its higher order generalizations. These can be represented in the harmonic domain by their associated spectra such as the skew-spectrum (Munshi & Heavens 2010) and its higher order generalizations (Munshi et al. 2011a, 2020). These spectra have already been used to analyse WMAP9 (Smidt et al. 2010) as well as Planck data (Planck Collaboration XVII2016b). They are useful tools to separate individual contributions and estimate systematics. In this paper, we will concentrate on the projected skew-spectrum and kurt-spectrum in the context of weak lensing surveys (Munshi et al. 2011c).

2019MNRAS.485.2235B__Schmalzing_&_Buchert_1997_Instance_1

Analyses of 21-cm signals are mainly based on traditional N-point correlation statistics. Beyond the simplest two-point function (power spectrum), higher-order correlations are quite non-trivial to calculate and sometimes they suffer from conceptual challenges. On the other hand, the Minkowski functionals (MFs) are extremely useful tools in quantitatively describing the morphology because, in principle, they contain information on all the higher-order moments. The MFs were first introduced in cosmology by Mecke, Buchert & Wagner (1994). Since then they have been extensively employed to study the morphology of the large-scale structure of the universe and the cosmic web (Schmalzing & Buchert 1997; Sahni, Sathyaprakash & Shandarin 1998; Sathyaprakash, Sahni & Shandarin 1998; Bharadwaj et al. 2000; Hikage et al. 2003; Bharadwaj, Bhavsar & Sheth 2004; Pandey & Bharadwaj 2008; Einasto et al. 2011; Wiegand & Eisenstein 2017) as well as the CMB (Schmalzing & Gorski 1998; Novikov, Feldman & Shandarin 1999; Novikov, Schmalzing & Mukhanov 2000; Hikage, Komatsu & Matsubara 2006). Since the reionization landscape is similarly rich in geometrical properties because of growth and overlap of ionized ‘bubbles’, studying the morphology of reionization using MFs is highly compelling and feasible. The physics underlying the reionization process is expected to be manifested in the geometry and morphology of H i and H ii regions. The ratios of MFs are introduced in Sahni et al. (1998) as Shapefinders that precisely assess the shape of an object by directly estimating its physical dimensions. Therefore, using MFs and Shapefinders of the ionization field, it should be possible to probe the physics of the high-redshift universe. For instance, if reionization is driven in a non-standard manner through large energy output from multiple quasar jets, the very first ionized bubbles might be filamentary and not spherical (as they would be for point-like sources like stars in galaxies). Clearly the 3D structure of cosmological reionization in this scenario would be very different from the standard mechanism of point-like sources.

2018ApJ...867...55X__Acero_et_al._2015_Instance_1

During the likelihood analysis, the normalizations and spectral parameters of all sources within 5° of 3FGL J1640.4-4634c, as well as the normalizations of the two diffuse backgrounds, are left free. First, we create a Test Statistic (TS) map by subtracting the emission from the sources and backgrounds in the best-fit model with gttsmap, which is shown in the top panel of Figure 1. Some residual emission are shown in this TS map. Then we add additional point sources with power-law spectra in the model. The accurate positions of these sources obtained using the gtfindsrc tool, together with their TS values, are listed in Table 1. Next, we adopt the position of 3FGL J1640.4-4634c provisionally provided by the 3FGL catalog (Acero et al. 2015) and investigate the spectrum of 3FGL J1640.4-4634c. We bin the data into 12 equal logarithmic energy bins from 1 to 500 GeV and repeat the same likelihood fitting for each energy bin. In the model, the normalization parameters of sources within 5° around 3FGL J1640.4-4634c and the two diffuse backgrounds are left free, while all spectral indices, except 3FGL J1640.4-4634c, are fixed. The 95% upper limits are calculated for energy bins with TS values smaller than 4. The resulting spectral energy distribution (SED) is shown in the bottom panel of Figure 1. An obvious spectral upturn is shown in the SED of 3FGL J1640.4-4634c at an energy of about 10 GeV. To test whether the upturn spectrum is intrinsic or is due to two overlapping sources, we do the same likelihood fitting using the events with energies of 1–10 GeV and 10–500 GeV, respectively. For each analysis, we create a TS map with all sources (except 3FGL J1640.4-4634c) included in the model, which are shown in the Figure 2. The TS maps show a clear difference between two energy bands, and the centroids of emission in both energy bands deviate from that of 3FGL J1640.4-4634c. We thus expect that 3FGL J1640.4-4634c should consist of two different sources (labeled as “Source A” for the 1–10 GeV source and “Source B” for the 10–500 GeV source), and the source in the 3FGL catalog is simply the sum of these two sources.

2020AandA...637A..44N__Kraus_(2018)_Instance_1

Among the existing IACT systems, HESS has the largest FoV and hence provides the highest sensitivity for the diffuse γ-ray flux. Its electron spectrum analysis technique could be directly used to obtain a measurement of the diffuse Galactic γ-ray flux above energies of several TeV in the Galactic Ridge (|l| 30°, |b| 2°) region; see Figs. 3 and 4. A multi-year exposure of HESS could be sufficient for detection of the diffuse emission even from regions of higher Galactic latitude. This is illustrated in Fig. 5, where thick blue and red data points show mild high-energy excesses of the electron spectra derived by Kraus (2018), Kerszberg et al. (2017), Kerszberg (2017) over broken power-law models derived from the fits to lower energy data. Comparing these excesses with the level of the IceCube astrophysical neutrino flux and with the Fermi/LAT diffuse sky flux from the region |b| > 7° (corresponding to the data selection criterium of HESS analysis Kerszberg et al. 2017; Kerszberg 2017) we find that the overall excess flux levels are comparable to expected diffuse γ-ray flux from the sky region covered by the HESS analysis (the quoted systematic error on the electron flux is Δlog(EFE) ≃ 0.4). The overall excesses within 805 and 1186 h of HESS exposures (Kraus 2018; Kerszberg 2017) are at the levels of >4σ for the analysis of Kraus (2018) and 1.7σ for the analysis of Kerszberg (2017). A factor-of-ten longer exposure (which is potentially already available with HESS) could reveal a higher significance excess at the level of up to 5σ. Such an excess is predicted in a range of theoretical models including interactions of cosmic rays injected by a nearby source (Andersen et al. 2018; Neronov et al. 2018; Bouyahiaoui et al. 2019) or decays of dark matter particles (Berezinsky et al. 1997; Feldstein et al. 2013; Esmaili & Serpico 2013; Neronov et al. 2018) or a large-scale cosmic ray halo around the Galaxy (Taylor et al. 2014; Blasi & Amato 2019).

2020MNRAS.495.4845C__Bernardi_et_al._2016_Instance_1

The exploration of the Universe out to times earlier than the point of complete reionization is rapidly advancing. One of the most informative probes of these epochs is the 21-cm line produced by hydrogen atoms in the neutral intergalactic medium (IGM) at redshifts z > 6. This line redshifts to frequencies below 200 MHz and can be detected by low-frequency radio telescopes. Global 21-cm experiments measure the spectrum of this line averaged over the sky. The first tentative detection of the Cosmic Dawn signal was recently made by the Low-Band implementation of the Experiment to Detect the Global EoR Signature (EDGES, Bowman et al. 2018). Other global 21-cm experiments, including the Large-Aperture Experiment to Detect the Dark Ages (Bernardi et al. 2016; Price et al. 2018), the EDGES High-Band (Bowman & Rogers 2010; Monsalve et al. 2017, 2018, 2019), and the Shaped Antenna measurement of the background RAdio Spectrum (Singh et al. 2017, 2018), provide upper limits on the signal from Cosmic Dawn and the Epoch of Reionization (EoR), ruling out some extreme astrophysical scenarios. A parallel effort is being made by interferometric radio arrays that are placing upper limits on the fluctuations of the 21-cm signal, including the Donald C. Backer Precision Array for Probing the Epoch of Reionization (Kolopanis et al. 2019), the Low Frequency Array (LOFAR, Patil et al. 2017; Gehlot et al. 2019; Mertens et al. 2020), the Giant Metrewave Radio Telescope (Paciga et al. 2013), the Murchison Widefield Array (Beardsley et al. 2016; Barry et al. 2019; Li et al. 2019; Trott et al. 2020), and the Owens Valley Radio Observatory Long Wavelength Array (Eastwood et al. 2019). The most recent upper limit reported by LOFAR (Mertens et al. 2020) made it possible to place (weak) upper limits on the temperature of the neutral gas and ionization state of the Universe at z = 9.1 (Ghara et al. 2020; Mondal et al. 2020). Upcoming arrays, including the Hydrogen Epoch of Reionization Array (DeBoer et al. 2017), the Square Kilometer Array (Koopmans et al. 2015), and the New Extension in Nancay Upgrading LOFAR (Zarka et al. 2012), will provide measurements of the power spectrum over a wide range of scales and redshifts.

2018AandA...610A..61P__Glampedakis_et_al._2006_Instance_1

It was soon realized that these frequencies are probably related to oscillations of the neutron star, and several groups tried to identify them as elastic oscillations of the crust (Duncan 1998; Messios et al. 2001; Strohmayer & Watts 2005; Piro 2005; Sotani et al. 2007; 2013, 2016; Samuelsson & Andersson 2007, 2009; Steiner & Watts 2009; Deibel et al. 2014), Alfvén oscillations (Cerdá-Durán et al. 2009; Sotani et al. 2008; Colaiuda et al. 2009), or coupled magneto-elastic oscillations (Levin 2006; 2007; Glampedakis et al. 2006; Gabler et al. 2011; 2012; Colaiuda & Kokkotas 2011; van Hoven & Levin 2011, 2012). The theoretical models based on the observed frequencies are very elaborate and may be able to constrain properties of high-density matter as found in the interior of neutron stars. Some of the models, for instance, require a superfluid component in the core of the star (van Hoven & Levin 2011, 2012; Glampedakis et al. 2011; Passamonti & Lander 2013, 2014; Gabler et al. 2013; 2016, and in prep.). Different models depend sensitively on the identification of the fundamental oscillation frequency, and may not explain all of the observed frequencies. Even when the fundamental frequency is identified, the interpretation and parameter estimation is not yet straightforward because of degeneracies in the parameter space. However, keeping other stellar parameters fixed, some general trends of the fundamental oscillation frequency can be summarized as follows (see Gabler et al. 2016, and in prep. for a detailed discussion): i) The frequency scales linearly with the magnetic field strength. ii) It decreases with increasing compactness (Sotani et al. 2008). The compactness is related to the hardness of the equation of state (EOS): Material with a stiff equation of state is harder to compress, leading to larger radii and hence lower compactnesses. iii) It can only reach the surface for significantly strong magnetic fields $\bar B\gtrsim\bar B_\text{outbreak}(\sqrt{c_s})$ B̄≳B̄outbreak(cs) , whose thresholds depend on the square root of the shear velocity (Gabler et al., in prep.).

2020ApJ...893...54Y___2021_Instance_1

The polarimetric observations with ALMA at 0.87 mm toward B335 were conducted during 2016 to 2018, consisting of 13 successful executions (project code: 2015.1.01018.S). In the observations, 40–47 antennae were used in the configurations with baseline lengths ranging from 15 to 1400 m. The pointing center was α(J2000) = 19h37d00.s89, δ(J2000) = +7°34′096. The on-source integration time was 7.4 hr. The observations were conducted with the full polarization mode and at the frequency ranges of 335.5–339.5 GHz and 347.5–351.5 GHz with a total bandwidth of 8 GHz. In these observations, J1751−0939 was observed as the bandpass calibrator, J1938 + 0448 or J1935 + 2021 as the gain calibrators, and J1924−2914 or J2000−1748 for polarization calibration. The flux calibration was performed with the observations of quasars or the asteroid, Pallas. The data were manually calibrated by the EA ARC node using the Common Astronomy Software Applications (CASA) of the version 5.1.1 (McMullin et al. 2007). We additionally performed self-calibration of the phase using the Stokes I data. Then the calibrated visibility data were Fourier-transformed with the Briggs weighting with a robust parameter of +0.5 to generate Stokes IQU images, and the images were cleaned using the CASA task tclean. The achieved synthesized beam is 019 × 017. The noise level in the Stokes I image is 40 μJy beam−1, and it is 9 μJy beam−1 in the Stokes Q and U images. When we generated the polarized intensity (Ip) map, we debiased the polarized intensity (Ip) with , where σQ, U is the noise level in Stokes Q and U (Wardle & Kronberg 1974; Simmons & Stewart 1985). To extract polarization detections, we first binned up the Stokes IQU and Ip maps to have a pixel size of 01, which is approximately half of the beam size, and computed polarization orientations and fractions. Thus, the minimal separation between two polarization detections is 01. The Stokes I and Ip maps with their original pixel size of 002 are presented below. The polarization detections are extracted when the signal-to-noise ratios (S/N) of both Stokes I and Ip are larger than three, and thus the expected uncertainties in the polarization orientations are ≲9°.

2021ApJ...922..140R__Hughes_et_al._1998_Instance_1

We fit the total foreground absorption column by applying two-component multiplicative absorption models, tbabs and tbnew. 8 8 https://pulsar.sternwarte.uni-erlangen.de/wilms/research/tbabs/ The absorption component tbabs accounts for the Galactic absorption column N H,Gal, and it is fixed at N H,Gal = 6 × 1020 cm−2 (Dickey & Lockman 1990). The second absorption component, tbnew, accounts for the LMC absorption column, N H,LMC and is varied in the fits. We use tbnew as the absorption column for LMC as it allows us to set individual elemental abundances associated with N H,LMC at their respective LMC values. Recent measurements of the LMC abundances based on the X-ray spectral analysis of SNRs (Maggi et al. 2016; Schenck et al. 2016) suggest ∼50% lower LMC abundance values for O, Ne, Mg, and Fe than the previously estimated values (Hughes et al. 1998). Our derived shock parameters are consistent (within statistical uncertainties), assuming either set of these LMC abundances in tbnew. We tie N H,LMC among all the fitted spectra, assuming no temporal variation in the LMC column for SNR 1987A. We obtain a best-fit total absorption column density, NH=NH,Gal+NH,LMC=2.17−0.22+0.22×1021cm−2 . This value is comparable to estimates of N H obtained by other X-ray analyses: 2.35−0.08+0.09×1021cm−2 (Park et al. 2006), 1.44−0.12+0.16×1021cm−2 (Zhekov et al. 2009), and 2.60−0.05+0.05×1021cm−2 (Alp et al. 2021). We note that more recent H i surveys have suggested much higher N H,Gal values toward the LMC, i.e., ∼(2.5–4) × 1021 cm−2 (Kalberla et al. 2005; Willingale et al. 2013; HI4PI Collaboration et al. 2016). By adopting these high Galactic columns, the overall fits are equally good, but this results in a negligible LMC column. The implied negligible LMC column does not appear to be reasonable because optical extinction estimates show that the LMC contribution is greater than the Milky-Way contribution (Fitzpatrick & Walborn 1990; France et al. 2011). A detailed analysis of the contribution of the Galactic N H toward total absorption column density is beyond the scope of our work. While this issue was similarly outlined by Alp et al. (2021) in relation to their analysis of the XMM-Newton data of SNR 1987A, we note that the total columns (Galactic + LMC) are generally consistent between either values of the Galactic column, and thus that the best-fit parameters in our spectral model fits (i.e., electron temperatures, ionization age, abundances, and volume-emission measures) are not significantly affected (within statistical uncertainties).

2021MNRAS.506.3313G__Bluck_et_al._2014_Instance_1

The B/T ratio of a galaxy is the fraction of total luminosity contributed by the bulge component of the galaxy. Bulges are the central component of disc galaxies which appear as central bright cores in galaxy images or as excess-light over the disc light in the inner region of galaxy light profiles. The B/T ratio is an important indicator of galaxy structure and is well correlated with several other physical quantities of interest in studies of galaxy evolution, such as galaxy morphology (Graham & Worley 2008), kinematics (Cappellari et al. 2013), stellar mass, and star formation rate (Bluck et al. 2014). The B/T ratio also directly determines the bulge luminosity which in turn correlates with the mass of the central supermassive black hole of the galaxy (Kormendy & Richstone 1995; Marconi & Hunt 2003; Kormendy & Ho 2013). Galaxies having different B/T ratios are thought to have undergone evolution along different evolutionary paths. A high value of B/T of a galaxy generally indicates its evolutionary history dominated by galaxy mergers (Hopkins et al. 2010). On the other hand, majority of low B/T systems are often pseudobulge hosting galaxies (Fisher & Drory 2008; Gadotti 2009) which are thought to undergo slow evolution through internal, secular evolution processes (Kormendy & Kennicutt 2004). It must be noted that unlike visual morphology which is a qualitative measure, the B/T ratio is a quantitative measure of the galaxy morphology. It is the only reliable way to separate ellipticals and disc galaxies when morphological features such as spiral arms cannot be resolved by the telescope (due to resolution or sensitivity limitations, particularly at high redshifts). The quantitative nature of the B/T ratio also allows direct comparison of galaxies with their counterparts in cosmological simulations. One can study these simulated galaxies to understand the origin of various properties of real galaxies. All these factors make the B/T ratio an important parameter to measure in studies of galaxy formation and evolution.

2019ApJ...887..174V__Barkana_&_Loeb_2001_Instance_1

Supermassive black holes (SMBH) with masses M• ∼ 109–1010 are recognized to be present at redshifts as high as z ≃ 6–7.5, when the universe was 650–800 Myr young (Fan et al. 2003; Willott et al. 2010; Mortlock et al. 2011; Bañados et al. 2014, 2018; Wu et al. 2015; Decarli et al. 2018; Izumi et al. 2019)—in total more than 150 such SMBHs are already known (see, e.g., Fan et al. 2019). Their origin remains elusive—it is unclear how massive their seeds were, how efficient their growth rate was, and what was the mass reservoir for their growth. To fit the existence of the quasars J0100 + 2802 (z = 6.33), J1120 + 0641 (z = 7.09), and J1342 + 0928 (z = 7.54) with SMBH masses M• = 1.2 × 1010, 2 × 109, and 7.8 × 108 respectively, one has to assume that their masses grow as M• = M•,0 exp [t/(47 Myr)], corresponding to the standard Eddington limit with a 10% radiative efficiency , the Salpeter growth time tS = cσT/(4πGmp) = 47 Myr, and the seed mass M•,0 ≥ 103 at z ≥ 40 (Bañados et al. 2018). In this scenario SMBHs have to begin growing even earlier than the very first stars are assumed to have appeared (see discussion in Barkana & Loeb 2001). Moreover, it suggests that the accretion is tightly tuned to the Eddington rate, which seems physically unlikely (see discussion in Haiman & Loeb 2001; Volonteri & Rees 2005; Haiman 2013; Alexander & Natarajan 2014; Madau et al. 2014). Lower-mass black holes with M• ∼ 100 , originated from Population III stars, are apparently unlikely to serve as seeds for growing SMBHs, because photoionization and photoheating from their massive progenitors strongly suppress further supply of cold mass onto the BH (Johnson & Bromm 2007), and would require even more time for the black hole to grow. Note, however, that this channel for SMBH seeds is currently widely discussed (see references in Natarajan et al. 2019). Also note that this problem of the presence of such enormously massive BHs in a younger than 1 Gyr universe can be to a certain extent eased when possible magnification of z > 6 SMBHs due to gravitational lensing is considered (Fan et al. 2019; Pacucci & Loeb 2019a, 2019b). Recent millimeter observations of the quasar J0100 + 2802 (z = 6.33) with the most massive BH, M• ∼ 1.2 × 1010 , known at z > 6, indicate strong lensing with a magnification factor of ∼450 (Fujimoto et al. 2019). As a result, the estimate of the SMBH mass may be reduced by more than an order of magnitude, though this still remains exceedingly large for a 1 Gyr universe ∼109 (Fujimoto et al. 2019). The fraction of so strongly magnified quasars is fairly low considering the very small typical angular size of such lenses, as a rule ≪1″ (see, e.g., Pei 1995; Bolton et al. 2008; Pacucci & Loeb 2019b).

2018MNRAS.476.2591V__Patton_et_al._2016_Instance_1

Galaxy interactions represent a fundamental component of our current view of hierarchical galaxy evolution. Studies based on both observations and simulations have shown that galaxy collisions and mergers can dramatically affect the galaxies undergoing the interaction, by, e.g. triggering nuclear activity (e.g. Kennicutt 1984; Kennicutt et al. 1987; Ellison et al. 2011, 2013a; Silvermann et al. 2011; Satyapal et al. 2014), producing colour changes (e.g. Larson & Tinsley 1978; Darg et al. 2010; Patton et al. 2011), disrupting morphologies (e.g. Kaviraj et al. 2011; Patton et al. 2016; Lofthouse et al. 2017), and altering the metallicities (e.g. Rupke et al. 2010; Perez, Michel-Dansac & Tissera 2011; Scudder et al. 2012; Torrey et al. 2012). The most evident effect driven by galaxy encounters is probably the triggering of new episodes of star formation, which can occur both in the pre-merger regime between first pericentre and coalescence (e.g. Nikolic, Cullen & Alexander 2004; Ellison et al. 2008, 2013b; Patton et al. 2011; Scudder et al. 2012), and in the post-merger phase, where the two nuclei of the interacting galaxies have merged together (e.g. Kaviraj et al. 2012; Kaviraj 2014; Ellison et al. 2013a). The idea that galaxy mergers have a strong impact on the star formation activity is supported by studies of Ultra-Luminous InfraRed Galaxies (ULIRGs), i.e. galaxies with IR luminosities exceeding 1012 L⊙ and characterized by star formation rates (SFRs) up to ∼1000 M⊙ yr−1 (e.g. Barnes & Hernquist 1991; Mihos & Hernquist 1994; Daddi et al. 2010; Scoville et al. 2015). Observations have revealed that the majority of ULIRGs reside in interacting systems (e.g. Sanders & Mirabel 1996; Veilleux, Kim & Sanders 2002; Kartaltepe et al. 2010, 2012; Haan et al. 2011). Nevertheless, ULIRGs are rare and extreme examples of highly star-forming galaxies. Most galaxy–galaxy interactions result in SFR increases of at most a factor of a few, as shown in both numerical simulations (e.g. Di Matteo et al. 2008) and observations of galaxy pairs and post-mergers (Ellison et al. 2008; Martig & Bournaud 2008; Jogee et al. 2009; Robaina et al. 2009; Scudder et al. 2012).

2021MNRAS.505..435S___2020_Instance_1

Detections of ionic, atomic, and molecular species in exoplanetary atmospheres serve as a unique and strong diagnostic of those chemical and dynamical processes driving their formation and evolution. Their detection and abundance measurements could act as indicators of planetary formation scenarios and reveal connections to the primordial protoplanetary disc and the host star (Williams & Cieza 2011; Mordasini et al. 2016; Madhusudhan et al. 2017). Furthermore, discoveries of atmospheric chemical species allow us to better understand various thermodynamical processes and chemistry, winds in the upper atmosphere (Goodman 2009; Snellen et al. 2010; Brogi et al. 2016; Madhusudhan et al. 2016; Wyttenbach et al. 2020), and to probe planetary interiors and various bulk properties through their abundances (Kite et al. 2016; Thorngren & Fortney 2019; Madhusudhan et al. 2020). A whole host of ions, atoms, and molecules have been detected through a variety of, often complementary, techniques, such as differential spectrophotometry using low-to-mid resolution spectroscopy (e.g. Gibson et al. 2012, 2017; Deming et al. 2013; Kreidberg et al. 2014; Kirk et al. 2016; Nortmann et al. 2016), and high resolution spectroscopic techniques (e.g. Redfield et al. 2008; Snellen et al. 2008; Rodler, Lopez-Morales & Ribas 2012; Birkby et al. 2013; Hoeijmakers et al. 2015, 2018, 2020; Brogi et al. 2016; Birkby et al. 2017; Žák et al. 2019; Ehrenreich et al. 2020). To date, ionic species such as Fe ii and Ti ii (Hoeijmakers et al. 2019), atomic absorption from Na, K, H α, and He (e.g. Redfield et al. 2008; Sedaghati et al. 2016; Casasayas-Barris et al. 2017; Spake et al. 2018; Chen et al. 2020; Seidel et al. 2020), and molecules such as H2O, CH4, and CO (e.g. Konopacky et al. 2013; Brogi et al. 2014; Fraine et al. 2014; Barman et al. 2015; Sing et al. 2016) have been detected through the aforementioned techniques. Needless to say that this list of detected constituents is by no means exhaustive, nor that of methods employed to detect exoplanetary atmospheres. For instance, high-resolution imaging instruments such as SPHERE (Beuzit et al. 2019) and GRAVITY (Gravity Collaboration et al. 2017), both at the VLT (ESO’s Very Large Telescope), through combination with low-dispersion spectroscopy, have facilitated direct measurements of exoplanetary atmospheres (Samland et al. 2017; Gravity Collaboration et al. 2020).

2020MNRAS.497.3504T__Migliori_et_al._2017_Instance_1

Galactic BHXBs offer excellent laboratories in which to study jet interaction zones, as their jets evolve on day to month time-scales, they are located at nearby distances, and they are thought to be good analogues for AGNs. Given their incredible diagnostic potential, over the last couple of decades there have been many observational campaigns searching for these highly sought after interaction sites near BHXBs. To date, several candidate jet–ISM interaction sites have been identified; SS 433 (Dubner et al. 1998), Cygnus X–1 (Gallo et al. 2005; Russell et al. 2007), 1E 1740–2942 (Mirabel et al. 1992), GRS 1758−258 (Martí et al. 2002), GRS 1915+105 (Rodríguez & Mirabel 1998; Chaty et al. 2001; Kaiser et al. 2004), H1743–322 (Corbel et al. 2005), XTE J1550–564 (Corbel et al. 2002; Kaaret et al. 2003; Migliori et al. 2017), XTE J1748–288 (Brocksopp et al. 2007), GRO J1655–40 (Hjellming & Rupen 1995; Hannikainen et al. 2000), GX 339-4 (Gallo et al. 2004), 4U 1755–33 (Kaaret et al. 2006), XTE J1752–223 (Yang et al. 2010; Miller-Jones et al. 2011; Yang et al. 2011; Ratti et al. 2012), XTE J1650–500 (Corbel et al. 2004), XTE J1908+094 (Rushton et al. 2017), 4U 1630–47 (Neilsen et al. 2014; Kalemci, Maccarone & Tomsick 2018), LMC X–1 (Russell et al. 2006; Cooke et al. 2007; Hyde et al. 2017), and GRS 1009–45 (Russell et al. 2006). From these past works, it is clear that finding and confirming interaction sites can be incredibly difficult and often observationally expensive (e.g. requiring deep, wide-field radio continuum observations). This difficulty mainly results from the fact that interaction sites can manifest with a wide variety of morphologies and emission properties, likely dependent on the properties of the BHXB (e.g. space velocity; Miller-Jones et al. 2008; Heinz et al. 2008; Wiersema et al. 2009) and/or local ISM properties (e.g. density; Heinz 2002; Kaiser et al. 2004). Once identified, detailed calorimetric calculations of interaction sites are highly sensitive to the properties of the ISM (i.e. density, kinetic temperature, shock velocity; Russell et al. 2007; Sell et al. 2015), and thus require accurate constraints on the physical conditions in the interacting gas, which cannot be derived with continuum observations alone. Therefore, developing and implementing new methods that allow us to identify and place improved observational constraints on these parameters at multiple interaction sites, is crucial for taking full advantage of the diagnostic potential of these regions.

2020MNRAS.499.4158G__Fialkov_&_Loeb_2016_Instance_1

In 2018, a deep spectral feature centred at 78 MHz was reported by the EDGES collaboration (Bowman et al. 2018). The feature was presented as the long sought-after 21-cm absorption feature seen against the CMB during the CD at z ∼ 17. The location of this putative absorption trough is consistent with redshift predictions from theoretical models and simulations of the CD (Furlanetto et al. 2006; Pritchard & Loeb 2010; Mesinger, Ferrara & Spiegel 2013; Cohen et al. 2017). However, the depth of the feature is ΔT21 ∼ 0.5 K ($99{{\ \rm per\ cent}}$ confidence level), which is two to three times stronger and considerably wider (Δν ∼ 19 MHz) than that predicted by the most optimistic astrophysical models (e.g. Pritchard & Loeb 2010; Fialkov, Barkana & Visbal 2014; Fialkov & Loeb 2016; Cohen et al. 2017). Moreover, the observed feature is flat-bottomed instead of a smooth Gaussian-like shape. Several ‘exotic’ theoretical models have already been proposed which might explain the depth of the feature, such as a considerably colder IGM due to interaction between baryons and dark matter particles causing a lower spin-temperature and therefore a deeper absorption feature (e.g. Barkana 2018; Fialkov, Barkana & Cohen 2018), or a stronger radiation background against which the absorption is taking place (e.g. Dowell & Taylor 2018; Ewall-Wice et al. 2018; Feng & Holder 2018; Fialkov & Barkana 2019). Although the 21-cm signal is expected to be stronger at these redshifts, the foreground emission is several times brighter at these frequencies compared to EoR 21-cm signal observations at 150 MHz (Bernardi et al. 2009, 2010). Moreover, ionospheric effects are amplified at lower frequencies (de Gasperin et al. 2018; Gehlot et al. 2018), rendering the measurement of the signal equally (or even more) challenging than in EoR experiments. As of now, Ewall-Wice et al. (2016) reported a systematics-limited power spectrum upper limit of $\Delta _{21}^2 \lt (10^4~\text{mK})^2$ on co-moving scales $k\lesssim 0.5~h\, \text{cMpc}^{-1}$ (in 3 h of integration) on the 21-cm signal brightness temperature in the redshift range 12 ≲ z ≲ 18 using MWA. This overlaps with the low-redshift edge of the 21-cm absorption feature (Bowman et al. 2018). Gehlot et al. (2019) provided a 2σ upper limit of $\Delta _{21}^2 \lt (1.4\times 10^4~\text{mK})^2$ on the 21-cm signal power spectrum at $k = 0.038~h\, \text{cMpc}^{-1}$ (in 14 h of integration) using the LOFAR-Low Band Antenna (LBA) system in the redshift range 19.8 ≲ z ≲ 25.2, which corresponds to the high-redshift edge of the absorption feature. More recently, Eastwood et al. (2019) used OVRO-LWA observations to report a 2σ upper limit of $\Delta _{21}^2 \lt (10^4~\text{mK})^2$ at $k \approx 0.1~h\, \text{cMpc}^{-1}$ (in 28 h of integration) at redshift z ≈ 18.4.

2018MNRAS.479.3254V___2000_Instance_2

The lifetime of molecular clouds (MCs) remains an active research topic in the study of the interstellar medium and star formation, and most recent studies, both observational and theoretical, place this lifetime at a few times 107 yr for clouds in the 105–106M⊙ mass range (e.g. Blitz & Shu 1980; Kawamura et al. 2009; Zamora-Avilés, Vázquez-Semadeni & Colín 2012; Zamora-Avilés & Vázquez-Semadeni 2014; Lee, Miville-Deschênes & Murray 2016). In addition, several observational studies have suggested that the star formation rate (SFR) of the clouds appears to increase over their lifetimes. For example, studies of young clusters embedded in moderate-mass MCs (∼104M⊙) (e.g. Palla & Stahler 1999, 2000; Da Rio et al. 2010) have shown that their age histograms contain a large majority of young (1–2 Myr) objects, but also a tail of older (up to several Myr) ones suggesting an accelerating star formation activity, sometimes followed by a subsequent decline (see also Povich et al. 2016; Schneider et al. 2018). In addition, Kawamura et al. (2009) reported a clear evolutionary process over the lifetime of giant molecular clouds (GMCs; of masses ∼105–106M⊙) in the Large Magellanic Cloud, evidenced by the increasing number of massive stars across the sequence of GMC ‘classes’ proposed by those authors. Finally, on the basis of the large scatter in the observed star formation efficiency (SFE) in Milky Way GMCs, Lee et al. (2016) have concluded that the SFR in those clouds must also be time variable. Numerical simulations of MC formation and evolution also exhibit time varying, increasing SFRs during their early stages (e.g. Vázquez-Semadeni et al. 2007; Hartmann, Ballesteros-Paredes & Heitsch 2012). Also, in the presence of stellar feedback, at late times the SFRs reach a maximum and begin to decrease again (e.g. Vázquez-Semadeni et al. 2010; Colín, Vázquez-Semadeni & Gómez 2013). Vázquez-Semadeni, González-Samaniego & Colín (2017) have recently shown that the simulations of Colín et al. (2013) in fact produce stellar age histograms highly resemblant of the observed ones (Palla & Stahler 1999, 2000; Da Rio et al. 2010), and reproduce observed radial age gradients in clusters (Getman et al. 2014) as well as bottom-heavy stellar initial mass functions (IMFs) in scattered regions of massive star formation (Povich et al. 2016).

2017MNRAS.469.3108C__Diamond-Stanic_et_al._2012_Instance_1

Still unconstrained are the mechanisms that are able to modify both SF and morphology and their corresponding time-scales. Several hypotheses have been proposed to explain the quenching of the SF in blue galaxies, such as gas stripping (e.g. Gunn & Gott 1972), morphological or gravitational quenching (Martig et al. 2009; Genzel et al. 2014), shock heating of infalling cold gas by the hot halo (Dekel & Birnboim 2006) or an exhaustion of the gas supply (e.g. Larson, Tinsley & Caldwell 1980). Moreover, in massive galaxies, the role of AGNs in influencing galaxy evolution and quenching SF is supported by several observations (Hopkins et al. 2005; Kaviraj et al. 2007; Diamond-Stanic et al. 2012; Fabian 2012, and references therein, Cimatti et al. 2013; Cicone et al. 2014; Förster Schreiber et al. 2014) and corroborated by the theoretical results obtained combining N-body simulations of dark matter haloes evolution (Springel et al. 2005; Boylan-Kolchin et al. 2009) with semi-analytic models for galaxy formation (White & Frenk 1991; Springel et al. 2005; Lu et al. 2011; Benson 2012). However, other models are capable of forming rapidly quiescent galaxies without invoking the AGN feedback (e.g. Khochfar & Silk 2006; Naab, Khochfar & Burkert 2006, 2009; Johansson, Thomas & Maraston 2012). Stellar or supernova (SN) feedback is most likely channel for the SF quenching in low-mass galaxies (e.g. 1010 M⊙; Kaviraj et al. 2007). Several mechanisms have also been invoked to explain the morphological transformation. Numerical simulations have shown that major merging can give rise to elliptical and S0 galaxies (Bekki 1998) and that also minor merging can play an important role in spheroid and bulge growth (Bournaud, Jog & Combes 2007; Naab et al. 2007). From an observational point of view, evidence that the morphological transformation can also be induced by environmental mechanisms (Larson et al. 1980; Farouki & Shapiro 1981; Moore et al. 1999; Quilis, Moore & Bower 2000; ; Bekki, Couch & Shioya 2002) or by the secular growth of pseudo-bulges (Courteau, de Jong & Broeils 1996; Norman, Sellwood & Hasan 1996; MacArthur, Courteau & Holtzman 2003; Kormendy & Kennicutt 2004; Debattista et al. 2006) has been found.

2022MNRAS.517.1218L__Barnes_&_Hernquist_1996_Instance_1

Several consortia have been actively approaching the question based on large photometric and spectroscopic surveys such as 2dF and SDSS, covering large sky areas and redshift ranges (Lewis et al. 2002; Balogh et al. 2004; Kauffmann et al. 2004; Poggianti et al. 2017). All these efforts have found indisputable evidence for the impact of environment in galaxy evolution (and references therein; Boselli & Gavazzi 2006, 2014; Cortese et al. 2021). However, understanding the role played by different physical mechanisms exerted under diverse environment conditions constitutes the matter of a very active debate. The involved physical mechanisms are classified in two types: hydrodynamic and gravitational. Hydrodynamic effects concern the stripping of cold/warm interstellar gas (H i and H2) by the hot intracluster medium (ICM). The ram-pressure stripping (RPS, Gunn & Gott 1972) and the viscous stripping (Nulsen 1982) are the most studied cases. On the other side, we have the tidal (gravitational) mechanisms occurring between a galaxy and the cluster potential (Byrd & Valtonen 1990) or among neighbour galaxies (Merritt 1983; Barnes & Hernquist 1996; Walker, Mihos & Hernquist 1996). These include major mergers, accretion of low-mass satellites, and the accumulation of fast speed encounters between galaxies (the galaxy harassment, Moore, Katz & Lake 1996). The removal of the galaxy halo gas, known as galaxy starvation (e.g. Larson, Tinsley & Caldwell 1980), is predicted to occur either by hydrodynamic or gravitational interactions. Most of these mechanisms are predicted to transform a spiral galaxy into an S0, and it is known that more than one process might act simultaneously on a single galaxy. The pre-processing of galaxies occuring within groups infalling towards clusters and seems to be particularly important (Donnari et al. 2021). Groups of galaxies are known to have lower velocity dispersions than clusters, allowing slower and deeper tidal interactions among their members. Several authors (Fadda et al. 2008; Poggianti et al. 2009b) provided substantial evidence that strong galaxy evolution is occurring in low-mass systems at a large distance from the cluster core. However, the debate pre-processingversuscluster effects remains open because many variables are involved, such as the infalling orbits, initial gas/stellar masses, the group/cluster properties, and even the surrounding large-scale structure (Rhee et al. 2020; Salerno et al. 2020).

2021ApJ...920L..31N__Sterling_et_al._2017_Instance_1

But if, as our studies in this Letter indicate, microstreams might be the result of accumulated and persistent velocity enhancements resulting from a series of switchbacks, then it could be that individual switchbacks result from coronal jets, and the microstreams are a consequence of a series of such jet-driven switchbacks occurring in close succession. Thus, this would be a modification of the idea put forth by Neugebauer (2012) whereby a series of minifilament eruptions capable of producing coronal jets could accumulate and generate a microstream. In fact, homologous jets, continuing for hours at a time, have been commonly observed (e.g., Chifor et al. 2008; Cheung et al. 2015; Panesar et al. 2016a, 2016b; Sterling et al. 2017; Joshi et al. 2017; Paraschiv & Donea 2019; Moore et al. 2021). Under the minifilament-eruption scenario, the multiple minifilament/flux ropes would be ejected as long as the cancellation continues (Panesar et al. 2016a; Sterling et al. 2017). A swarm of such homologous jets, produced over a several-hour time period, conceivably could account for a microstream peak. Additionally, there is some recent evidence (Bale et al. 2021; Fargette et al. 2021) that switchbacks have an extent similar to the scale size of supergranules (∼30,000 km). Measurements of the lengths of the erupting minifilaments that produce jets range from ∼8000 km (Sterling et al. 2015) to ∼18,000 (Panesar et al. 2016a), and thus of similar order to (albeit somewhat smaller than) a typical supergranule diameter. Fargette et al. (2021) also found switchbacks to occur on another size scale, one that approximately corresponds to the size of photospheric granules, ∼1000 km. Chromospheric spicules have widths of some fraction of this size, and thus their observation could be consistent with some spicules resulting from the minifilament-eruption-jet mechanism as suggested in Sterling & Moore (2016), and then those spicules making smaller-scale switchbacks as suggested in Sterling & Moore (2020).

2017ApJ...839...56T___2013_Instance_2

Mid-infrared and millimeter polarimetric observation has so far been considered as the best method to probe the magnetic field. This is because if aspherical grains in disks become aligned with the magnetic field as is the case in the interstellar medium (ISM), the polarization vector arising from thermal emission of the aligned grains becomes perpendicular to the local magnetic field line (Cho & Lazarian 2007, henceforth CL07, Matsakos et al. 2016; Yang et al. 2016b; Bertrang et al. 2017). At mid-infrared wavelengths, Li et al. (2016) performed a polarimetric imaging observation of AB Aur using CanariCam. As a result, they detected a centrosymmetric polarization pattern, and the degree of polarization was as high as 1.5% at large radii. At millimeter wavelengths, polarimetric observations of disks have been performed (e.g., Hughes et al. 2009, 2013; Rao et al. 2014; Stephens et al. 2014; Cox et al. 2015; Kataoka et al. 2016b). Polarized emission from a circumstellar disk has been detected in the Class 0 phase (Rao et al. 2014; Cox et al. 2015). More evolved disks do not show a degree of linear polarization larger than 0.5% (Hughes et al. 2009, 2013). It should be mentioned that Stephens et al. (2014) detected polarized flux from HL Tau, which is classified as a Class I-II, with an average degree of linear polarization of 0.9%. More recently, Kataoka et al. (2016b) reported the first submillimeter polarization observation of a disk obtained with ALMA, and they clearly detected polarized flux from HD 142527. The polarization fraction at a peak position of polarized intensity was 3.26%, and the maximum polarization fraction was as high as 13.9%. The disk reveals radial polarization vectors; however, they flip by 90° in its northeast and northwest regions. In addition, the detected polarization fraction is much larger than the stringent limit set by Hughes et al. (2009, 2013), and further polarimetric observations by ALMA will reconcile this discrepancy.

2019MNRAS.487..475C__Ward-Thompson_et_al._2009_Instance_2

We attempted to find a correlation between the mean magnetic field and the outflow and minor axis of the cloud CB 17. Relative orientations between various quantities of CB 17 are presented in the first row of Table 6, along with a comparative study of the same for some dark clouds. The first column gives the cloud ID and columns 2–6 give the position angles of the mean magnetic field at the envelope ($\lt \theta ^{\rm env}_B\gt $), mean magnetic field at the core ($\lt \theta ^{\rm core}_B\gt $), outflow axis (θout), minor axis (θmin) of the core of the cloud and Galactic plane (θGP), respectively. $\lt \theta ^{\rm env}_B\gt $ of CB 17 is found to be almost aligned along the Galactic plane over that region of the sky (column 7), which indicates the dominance of the Galactic magnetic field over the envelope magnetic field of the cloud and thus we cannot infer much about the magnetic field structure from the optical study. A similar feature has also been observed in the cases of CB 34 (Das et al. 2016), L328, L673-7 (Soam et al. 2015), CB 26 (Halder et al. 2019), CB 3 and CB 246 (Ward-Thompson et al. 2009). However, $\lt \theta ^{\rm core}_B\gt $ of CB 17 (obtained by submm polarimetry) turned out to be perpendicular to $\lt \theta ^{\rm env}_B\gt $ (column 8); a similar phenomenon has been observed in the case of CB 34-C1 (Das et al. 2016), IRAM 04191 (Soam et al. 2015) and CB 54 (Wolf et al. 2003). Since, in the case of CB 17, $\lt \theta ^{\rm env}_B\gt $ is along the Galactic plane orientation, this implies that only $\lt \theta ^{\rm core}_B\gt $ (denser region) is linked with the ongoing physical phenomena in the cloud. $\lt \theta ^{\rm core}_B\gt $ is oriented perpendicular to θGP as well (column 9) and a similar orientation has been observed in the case of CB 34-C1 (Das et al. 2016), IRAM 04191 (Soam et al. 2015), CB 230 and CB 244 (Wolf et al. 2003) as well. Moreover, $\lt \theta ^{\rm core}_B\gt $ is found to be almost aligned along the minor axis of the core of the cloud; the angular offset is nearly 5.9° (column 10). The alignment of $\lt \theta ^{\rm core}_B\gt $ with the minor axis of the cloud fits the magnetically regulated star formation model, in which the magnetic field should lie along the minor axis of the cloud (Mouschovias & Morton 1991; Li 1998), and the same feature has also been observed for the clouds CB 34-C1 (Das et al. 2016) and IRAM 04191 (Soam et al. 2015). The angular offset between $\lt \theta ^{\rm core}_B\gt $ and the outflow axis is found to be 80.9° (column 11), that is, the core-scale magnetic field is oriented almost perpendicular to the outflow direction and a similar phenomenon has also been observed in the case of CB 34 (Das et al. 2016), CB 68 (Bertrang et al. 2014), B335, CB 230, CB 244 (Wolf et al. 2003) and CB 3 (Ward-Thompson et al. 2009). The angular offset between θout and θmin is found to be 75° and the same feature has been observed for CB 34-C1 (Das et al. 2016).

2019MNRAS.487.1210T__McNamara_&_Nulsen_2007_Instance_1

On larger scales, the clusters in which BCGs reside can generally be divided into two categories: cool core clusters, which exhibit very peaked surface brightness distributions at X-ray wavelengths, and non cool core clusters, with similar overall X-ray luminosities but with smoother, less peaked X-ray surface brightness distributions. Some authors (e.g. Hudson et al. 2010; Santos et al. 2010) define an intermediate category called moderate or weak cool core clusters. Since cool core clusters have short radiative cooling time-scales on the order of 108 yr in their centres (e.g. Voigt & Fabian 2004; McNamara & Nulsen 2007, 2012; Hlavacek-Larrondo et al. 2012), starbursts are expected to be common at the centre of such clusters. Indeed, the central cool gas in these clusters should condense onto the BCG, forming stars at rates of hundreds of solar masses per year (e.g. Fabian 1994). However, most BCGs are relatively quiescent and those that do show evidence of star formation generally tend to have star formation rates 1 order of magnitude smaller, on the order of $1-150 \, \mathrm{M_{\odot }\, {yr}^{-1}}$ (e.g. Donahue et al. 2007; Bildfell et al. 2008; O’Dea et al. 2008, 2010; Rawle et al. 2012). This mismatch between expected and observed star-forming rates, known as the cooling flow problem, is thought to be caused by active galactic nuclei (AGNs) feedback processes from the BCG. AGNs can release copious amounts of energy into the intracluster medium (ICM) through many ways, including: jetted outflows that inflate cavities, weak shocks, sound waves, or turbulence in the ICM (e.g. Markevitch & Vikhlinin 2007; McNamara & Nulsen 2007, 2012; Zhuravleva et al. 2014; Fabian et al. 2017). Alone, the energy released by jetted outflows appears to be on the same order as the energy needed to offset cooling (e.g. Rafferty et al. 2006; McNamara & Nulsen 2007; Hlavacek-Larrondo et al. 2012), therefore suggesting that AGN feedback is a good candidate for solving the cooling flow problem.

2022MNRAS.515.1942D___2022b_Instance_1

Comparison of 1D marginal posterior distributions over the parameters S8 ≡ σ8(Ωm/0.3)0.5, σ8 and Ωm, from DES Y3 data as well as other experiments, and consistency tests for this work (in blue). (a) Constraints obtained from the harmonic (this work) and real (Amon et al. 2022; Secco et al. 2022) space analyses of DES Y3 data are shown in blue and green (see also Fig. 14), both with and without shear ratio information (SR; Sánchez et al. 2021). (b) Constraints from other weak lensing surveys, namely HSC Y1 (Hikage et al. 2019; Hamana et al. 2020, 2022b), KiDS-1000 (Asgari et al. 2021), and KiDS-450 (Hildebrandt et al. 2017; Köhlinger et al. 2017) are shown in grey, and constraints from cosmic microwave background observations from Planck 2018 are shown in yellow (Planck Collaboration VI 2020). (c) Constraints from four weak lensing analyses of DES Y3 data are compared, including the analysis of mass map moments (Gatti et al. 2021b) and peaks (Zürcher et al. 2022), and illustrating a high level of consistency (see also Fig. 15). (d) Consistency tests where redshift bins are removed one at a time (first four) and where the data vector is split into its large- and small-scale data points (last two) (see also Appendix C). (e) Various other consistency tests: removing autopower spectra, swapping the covariance matrix, and marginalizing over redshift distribution uncertainties with HyperRank and MultiRank(see also Appendix C). (f) Modelling robustness test for intrinsic alignment (IA), including B-mode power spectra, or replacing TATT by NLA, or removing IA contributions altogether (see also Section 6.2, Fig. 12). (g) Other robustness test, freeing the dark energy equation-of-state w or fixing the neutrino mass to 0.06 eV. (h) Baryonic feedback tests where the matter power spectrum is computed with HMCode instead of HaloFit, and fiducial scale cuts are replaced with kmax = 1, 3, and 5 $h\, {\rm Mpc}^{-1}$ scale cuts (see also Section 6.3 and Fig. 13).

2018AandA...614A..56B__Chung_et_al._2007_Instance_1

The studies of emission lines, however, which for wide-field cameras require specific and expensive narrow-band (NB) filters of large physical size, have thus far been limited to pointed observations. Very deep Hα observations of a few galaxies in nearby clusters, including our recent observations with MegaCam, have led to several intriguing discoveries. They have shown that the ionised phase appears to be an ideal tracer of stripped gas in dense regions: approximately 50% of late-type galaxies show extended (~50 kpc) tails of ionised gas with surface brightness Σ(Hα) of approximately a few 10−18 erg s−1 cm−2 arcsec−2 (Boselli & Gavazzi 2014), while only a handful of galaxies have extended cold or hot gaseous tails (Chung et al. 2007; Sun et al. 2006, 2007, 2010; Scott et al. 2012; Sivanandam et al. 2014; Jáchym et al. 2014). In some objects, the cometary shape of the tails indicates that the gas has been stripped by the interaction with the hot ICM (Gavazzi et al. 2001; Yoshida et al. 2002; Yagi et al. 2010; Fossati et al. 2012, 2016, 2018 – paper II; Zhang et al. 2013; Boselli et al. 2016a); in other systems, bridges of ionised gas linking different nearby galaxies are associated with tidal tails in the stellar component, suggesting gravitational perturbations with nearby companions or within infalling groups (i.e. pre-processing; Kenney et al. 2008; Sakai et al. 2002; Gavazzi et al. 2003a; Cortese et al. 2006). They have also shown that within the tails of stripped gas, star formation in compact HII regions occurs in some but not in all objects (Gavazzi et al. 2001; Yoshida et al. 2008; Hester et al. 2010; Fumagalli et al. 2011a; Fossati et al. 2012; Boissier et al. 2012; Yagi et al. 2013; Kenney et al. 2014; Boselli et al. 2016a, 2018 – paper III). The removal of the gas affects the activity of star formation of galaxies on different timescales that depend on the perturbing mechanism (Larson et al. 1980; Boselli et al. 2006, 2016b; Bekki 2009, 2014; McGee et al. 2009; Cen 2014; Fillingham et al. 2015; Rafieferantsoa et al. 2015). The distribution and the morphology of the star-forming regions within galaxies is also tightly connected to the perturbing mechanisms (increases in the nuclear star formation activity and asymmetric distributions of star-forming regions are typical in gravitational interactions, radially truncated star-forming discs in interactions with the ICM, and fainter star formingdiscs in starvation, Kennicutt & Keel 1984; Barton et al. 2000; Boselli et al. 2006; Ellison et al. 2008; Woods et al. 2010; Scudder et al. 2012; Patton et al. 2011, 2013). All these pieces of evidence underline the power of NB Hα imaging data in identifying the dominant perturbing mechanism in dense environments.

2022MNRAS.513.4361M__Zdziarski,_Johnson_&_Magdziarz_1996_Instance_1

The relxillDCp model combines the ionized disc reflection code xillverDCp (García et al. 2013, 2016) with the convolution model relconv (Dauser et al. 2013). The relconv model determines the relativistic effects in the reflection spectrum and assumes a broken power-law emissivity profile for the illumination of the disc by an X-ray corona. The emissivity profile has the following form: $\epsilon (r)\propto r^{-q_{{\rm in}}}$ for rin ≤ r ≤ rbr, and $\epsilon (r)\propto r^{-q_{{\rm out}}}$ for $r_{\rm br}\le r\le r_{{\rm {\rm out}}}$, where qin and qout represent inner and outer emissivity indices, respectively, rbr is the break radius, rin and rout are the inner and outer disc radii, respectively. The other parameters that characterize the disc reflection model are: black hole spin (a), the inclination angle (θ○) of the disc to the observer, reflection fraction (refl_frac), iron abundance (AFe), ionization state (log ξ), and number density (ne) of electrons in the disc atmosphere. The disc irradiation profile is considered Newtonian over the outer regions of the disc, and hence we fixed the outer emissivity index at 3. We fixed the break radius at 6rg, which is a typical AGN coronal radius (e.g. Wilkins & Fabian 2011; Mallick et al. 2021). We assume the solar abundance of iron and fixed the inner and outer radii of the accretion disc at the innermost stable circular orbit (risco) and 1000rg (rg = GMBH/c2), respectively. We set refl_frac=−1 in the relxillDCp model to solely describe the disc reflection. Since relxillDCp considers the thermal Comptonization model nthComp (Zdziarski, Johnson & Magdziarz 1996; Źycki, Done & Smith 1999) as the irradiating (primary) continuum, we replaced the zpowerlw continuum with nthComp. The slope of the nthComp and relxillDCp components are tied. The seed photon temperature in the nthComp model was set at the maximum possible disc temperature (see column 8 of Table 2) for each source. We fixed the electron temperature of the hot coronal plasma at a typical value of 100 keV. The relative strength of reflection was measured as a ratio of the disc reflected flux to the irradiating primary source flux in the 0.3–10 keV band. We find that the broad-band best-fitting spectral model is Tbabs×(relxillDCp+nthComp) for all the sources in the sample, except for J1559 and POX 52. The source, J1559, showed an absorption feature at ∼0.7 keV, which was modeled by a Gaussian absorption line gabs. The gabs component improves the fit statistic by Δχ2 = 92.9 for 3 free parameters. The expression for the broad-band best-fit model of J1559 is Tbabs×gabs×(zgauss+relxillDCp+nthComp). To model the absorption curvature present in the 1–2 keV band of POX 52, we included an ionized partial covering absorption component (zxipcf; Reeves et al. 2008). The zxipcf model has three free parameters: column density (NH,wa), ionization state (log ξwa) of the absorbing medium and covering fraction (Cf). The inclusion of the ionized absorption component provided an improvement of Δχ2 = 237.5 for three additional free parameters. The maximum-likelihood ratio (MLR) test suggests that the zxipcf model component is ≥99.99 per cent significant. The best-fitting model for POX 52 has the following expression: Tbabs×zxipcf×(relxillDCp+nthComp). We find that the relativistic reflection from an ionized, higher density accretion disc can self-consistently explain the soft X-ray excess emission for the low-mass AGN sample. The best-fitting unfolded spectral models, model components, and data-to-model ratio plots are shown in Fig. 1. We show the broad-band photon count spectra, best-fitting models, and residuals plots in Fig. A2.

2016ApJ...827..107P__Padoan_&_Nordlund_1999_Instance_1

To estimate the total integrated intensity of a shock-excited molecular line coming from a GMC, the shock models are scaled up so that the total energy dissipated in shocks is equal to the expected turbulent energy dissipation rate of the molecular cloud, as done in Basu & Murali (2001) and Pon et al. (2012). The dissipation rate of the turbulent energy of a molecular cloud, Lturb, is 2 where ρ is the gas density, σ is the one-dimensional velocity dispersion, R is the radius of the (spherical) cloud, and κ is the ratio of the dissipation timescale to the flow crossing timescale of the cloud. For this paper, κ is taken to be equal to 1, in agreement with numerical simulations of decaying turbulence (Gammie & Ostriker 1996; Mac Low et al. 1998; Stone et al. 1998; Mac Low 1999; Padoan & Nordlund 1999; Ostriker et al. 2001). This value of κ, however, is relatively uncertain, such that the predicted integrated intensities should only be considered to be accurate to a factor of a few. The integrated intensity of any shock-powered line, assuming a mean mass per hydrogen nuclei of 2.77 amu, is thus 3 where is the fraction of the shock energy being emitted in the line. While the total turbulent energy of the cloud depends on the cube of the radius, the conversion of a luminosity to an intensity introduces an r−2 dependence, and setting the dissipation timescale to be equal to the turbulent crossing time introduces a further r−1 dependence, such that this predicted integrated intensity is independent of the size of the cloud. Pon et al. (2012) show that if the velocity distribution of gas particles in a molecular cloud is Gaussian and isotropic, then the shock velocity at which the peak energy dissipation occurs is approximately 3.2 times larger than the one-dimensional velocity dispersion of the gas, since the energy dissipation rate of a shock scales with the third power of the shock speed, but the probability of gas particles having a particular velocity difference decreases with increasing velocity. We assume that all of the turbulent energy in a cloud is dissipated at a shock speed of 3 km s−1, which would be the peak energy dissipation velocity for a velocity dispersion of roughly 1 km s−1 that would lead to observed FWHMs of 2.3 km s−1. While this velocity dispersion is on the lower end for what is usually associated with IRDCs (e.g., Paper II), slightly larger velocity dispersions should create larger peak temperatures and larger energy dissipation rates, such that our shock models can be considered to be conservative estimates for the shock emission. See Pon et al. (2012) for a more detailed discussion about this method of scaling the shock models.

2019MNRAS.487.1626Q__Fender_2006_Instance_1

In the coupled ADAF-jet model, the accretion flow ADAF and the jet are connected by a defined parameter, $\eta \equiv \dot{M}_{\rm jet}/\dot{M}$, and $\dot{M}_{\rm jet}$ is input by assuming a value of, η, which is free parameter in the present model. The half-opening angle ϕ of the jet in the low/hard state of NS-LMXBs is uncertain. In this paper, we fix ϕ = 0.1 as assumed by several other authors for modelling the SED of the BH-LMXBs (e.g. Yuan et al. 2005; Zhang et al. 2010). Observationally, the bulk Lorentz factor of the jet in the low/hard state of X-ray binaries can be restricted in a relatively narrow range and the velocity of the jet is mildly relativistic, i.e. Γjet ≲ 2. More strictly, the bulk Lorentz factor is restricted to be as Γjet ≲ 1.67 (Gallo et al. 2003), and Γjet ≲ 1.2 (Fender 2006). In the internal shock model, the energy density of the internal shock increases with increasing Γjet, which finally will result in an increase of both the radio emission and X-ray emission (Yuan et al. 2005). However, since Γjet is restricted in a very narrow range by observations, we expect that a slight change of Γjet will result in a slight change of the jet emission. In this paper, we fix Γjet = 1.2 corresponding the bulk velocity of the jet 0.55c (Fender 2006). The value of ϵe and ϵB describing the fraction of the internal energy of the internal shock stored in the accelerated electrons and the magnetic field, and the index, pjet, describing the power-law distribution of the electrons in the jet after the acceleration by the shock are uncertain. Qiao & Liu (2015) tested the effect of ϵe and ϵB on the emergent spectrum of the jet in an observationally inferred range of 0.01 ϵe 0.1 and 0.01 ϵB 0.1. It was found that a change of ϵB in the range of 0.01–0.1, the emergent spectrum of the jet nearly does not change (see the right-hand panel of fig. 3 of Qiao & Liu 2015). A change of ϵe in the range of 0.01–0.1, the radio spectrum nearly does not change. However, the X-ray luminosity changes obviously by changing the value of ϵe from 0.01 to 0.1 (see the left-hand panel of fig. 3 of Qiao & Liu 2015). As shown in the left-hand panel of fig. 2 of Qiao & Liu (2015), the X-ray emission is completely dominated by the accretion flow (corona) in the luminous X-ray state, which is also true in this paper, i.e. the X-ray emission from the ADAF completely dominates the X-ray emission from the jet. In this paper, we fix ϵe = 0.04 and ϵB = 0.02, respectively, throughout the paper as (Qiao & Liu 2015). The value of the power-law index pjet of the electron distribution in the jet predicted by the shock acceleration is 2 pjet 3. By modelling the SEDs of three BH-LMXBs, the value of the power-law index pjet of the electron distribution is constrained to be 2.1 (Zhang et al. 2010). Meanwhile, a change of pjet in the range of 2 pjet 3 has very minor effect on the X-ray spectrum. In this paper, we fix the power-law index pjet = 2.1 throughout the paper.

2016ApJ...817..117S__Brisken_&_Zirin_1997_Instance_1

Running penumbral waves in velocity and intensity observations were first reported by Giovanelli (1972) and Zirin & Stein (1972). Later, they were found in the photosphere as well (Musman et al. 1976), but there they appear to be more intermittent and to have higher radial phase velocity (40–90 km s−1) than the waves in Hα. Whereas the velocity amplitudes are less in the photosphere than in the chromosphere, the density is very low there and most of the wave energy lies in the photosphere and subphotosphere. Larger amplitudes on the disk-side penumbra demonstrate an alignment of the oscillations along the magnetic field. Running waves are also detected in the umbra, but the waves were believed to be unrelated to those in the penumbra (Kobanov & Makarchik 2004). In the chromosphere, the frequency of travelling waves decreases as they propagate from the umbra into the outer penumbra (e.g., Lites 1988). A similar effect is also found in measurements of the propagation velocity of travelling waves (Brisken & Zirin 1997; Sigwarth & Mattig 1997; Alissandrakis et al. 1998; Kobanov & Makarchik 2004; Tziotziou et al. 2006, 2007). Generally, the waves decelerate from 40 km s−1 near the inner part of the penumbra to 10 km s−1 or less near the outer edge of the penumbra. More recently, from a multi-wavelength study including the coronal channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), Jess et al. (2015) revealed the presence of a wide range of frequencies, with longer periodicities preferentially occurring at increasing distance from the umbra. The phase speeds also tend to decrease with increasing periodicity as the waves propagate away from the umbral barycenter. These observations also suggest that these slow waves are driven by a regular coherent source. The physical nature of running penumbral waves has been controversial. Some researchers have regarded them as trans-sunspot waves originating from umbral oscillations since they detected waves starting from the umbra and propagating through the penumbra (e.g., Alissandrakis et al. 1992; Tsiropoula et al. 1996, 2000). However, others suggest that the trans-sunspot (i.e., outward) motion is apparent to a given line of sight, and that these oscillations actually represent the upward propagation of field-guided magnetoacoustic waves from the photosphere (e.g., Christopoulou et al. 2000, 2001; Georgakilas et al. 2000; Rouppe van der Voort et al. 2003; Bogdan & Judge 2006; Kobanov et al. 2006; Bloomfield et al. 2007; Jess et al. 2013, 2015). The gradual change in the inclination of the penumbral field lines is responsible for changes in the oscillation periods and phase speeds.

2017ApJ...837..127G__Kasliwal_et_al._2015_Instance_1

Even though the physical processes giving rise to the flaring emission of blazars remain debatable, considerable progress has been made in characterizing the statistical properties of blazar variability at different wavelengths, and in different time domains. In particular, it has been demonstrated repeatedly that the power spectral density (PSD) of blazar light curves is, in general, of a power-law form (Simonetti et al. 1985; Kataoka et al. 2001; Brinkmann et al. 2003; Papadakis et al. 2003; Aharonian et al. 2007; Ciprini et al. 2007; Chatterjee et al. 2008, 2012; Abdo et al. 2010a; Carini et al. 2011; Kastendieck et al. 2011; Edelson et al. 2013; Nakagawa & Mori 2013; Max-Moerbeck et al. 2014; Park & Trippe 2014; Revalski et al. 2014; Sobolewska et al. 2014; Aleksić et al. 2015; Isobe et al. 2015; Kasliwal et al. 2015). A physical process with such a variability power spectrum, denoted hereafter as , where νk is the temporal frequency (corresponding to the timescale 1/νk), A is the normalization constant, and β is the spectral slope, is called white noise when β = 0, flicker (pink) noise when β = 1, and Brownian (red) noise when β = 2 (Press 1978). The PSD integrated over some variability frequency range is then a measure of the variance of the underlying signal in the time series within the corresponding range of variability timescales. Breaks in the slope or in the normalization of a PSD may appear, signaling characteristic/critical variability timescales in the system. In the case of blazars, various segments of radio, optical, X-ray, and γ-ray power spectra within the variability time domains from years to days (and in some instances, even sub-hour timescales), are characterized by spectral slopes 1 ≤ β 3, meaning that the variability amplitude increases with increasing variability timescale. Rarely, however, have blazar PSDs been analyzed in a systematic way at different wavelengths across the electromagnetic spectrum and over a truly broad range of temporal frequencies. It is important to note that colored-noise-type power spectra are expected to flatten on longer variability timescales (to preserve the total finite variance), and to cut-off at frequencies corresponding to the shortest variability timescale in a system. The detection of such cutoffs in blazar periodograms would be of primary importance for constraining the physics of blazar jets; however, such detections may be hampered by the finite duration of available monitoring blazar data on the one hand, and statistical fluctuations resulting from the measurement errors on the other hand.

2021MNRAS.502..915C__Ogilvie_2014_Instance_1

Under the Applegate model, the change in orbital period is directly related to the change in the companion star’s gravitational quadrupole moment Q (Applegate & Patterson 1987), (8)$$\begin{eqnarray*} \frac{\Delta P_{\rm orb}}{P_{\rm orb}} = -9\frac{\Delta Q}{M_{\rm c} A^2}, \end{eqnarray*}$$where A = x(1 + q)/sin i is the orbital separation. For comparison, the total quadrupole moment induced by the spin of the companion star and the tidal distortion in the pulsar’s gravitational field is (Voisin, Breton & Summers 2020a) (9)$$\begin{eqnarray*} \frac{Q}{M_{\rm c} A^2} = -\frac{2}{9} k_2 \left(\frac{R_{\rm c}}{A}\right)^5 \left(4 q + 1\right), \end{eqnarray*}$$where Rc is the radius of the companion star and k2 is the apsidal motion constant, a parameter describing the deformability of the companion star (Sterne 1939). For solar-type stars k2 ∼ 0.035 (Ogilvie 2014), while if we assume that redback companions are akin to the companions in CV systems whose outer envelopes have also been stripped through accretion, then we may expect a smaller value k2 ∼ 10−3 (Cisneros-Parra 1970). For J2039, the hyperparameter $h = 3.9^{+2.2}_{-1.2}$ s corresponds to the typical fractional amplitude for the variations in orbital phase. Taking the simpler squared exponential covariance function of equation (4) corresponding to n → ∞, then the deviations in orbital period have covariance function, (10)$$\begin{eqnarray*} K_{\Delta P_{\rm orb}/P_{\rm orb}}(t_1,t_2) &=& \frac{\partial ^2 K}{\partial t_1 \partial t_2} \nonumber\\ &=& \frac{h^2}{l^2} \exp \left(\!-\frac{(t_1 - t_2)^2}{2\ell ^2}\!\right) \left(\!1 - \frac{(t_1 - t_2)^2}{\ell ^4}\!\right). \end{eqnarray*}$$The typical (fractional) amplitude of the orbital period variations is therefore ΔPorb/Porb ∼ h/ℓ = (3 ± 1) × 10−7, corresponding to $\Delta Q / Q \sim 3\times 10^{-5} k_2^{-1}$. The time-varying component to the gravitational quadrupole moment is therefore required to be of order a few per cent of the total expected quadrupole moment at most to explain the observed orbital period variations. From this, it seems plausible that the observed period variations can be powered by quadrupole moment changes, without requiring that a large fraction of the star be involved in the process. The required fractional quadrupole moment changes are very similar to those recently calculated for the companion to the black widow PSR J2051–0827 by Voisin et al. (2020b), despite the large difference in their masses.

2015AandA...576A..26K__Hathaway_&_Rightmire_2010_Instance_1

We have seen that some of our runs show clear activity cycles. Therefore we expect to see a corresponding modulation of the flow. In Fig. 13, we show for Run A the temporal variation of the mean large-scale magnetic field (\hbox{$\mean B$}B) normalized by Beq, the latitudinal component of the meridional circulation uθ(r, ± 32°) at r ≈ 0.95 R⊙ and r ≈ 0.73 R⊙, the mean rotation rate \hbox{$\mean\Omega(0.95~R_\odot,\pm32^\circ)$}Ω(0.95R⊙,±32◦), \hbox{$\overline\Omega(r,0^\circ)$}Ω(r,0◦) at r = 0.73 R⊙ and r = 0.95 R⊙, as well as the latitudinal and radial differential rotation \hbox{$\Delta_\Omega^{(r)}$}ΔΩ(r) and \hbox{$\Delta_\Omega^{(\theta)}$}ΔΩ(θ), defined in Eq. (13). We see that the meridional circulation varies with the magnetic field, becoming weaker during maximum and stronger during minimum, the overall temporal variation being about 50% in this case. (The linear correlation coefficient between \hbox{$\mean B$}B and uθ(0.95 R⊙, ± 32°) ≈ −0.36, − 0.38.) This kind of weak anti-correlation between the activity cycle and the meridional flow has been found in solar observations (Chou & Dai 2001; Hathaway & Rightmire 2010) and is believed to arise at least in part from the Lorentz force of the dynamo-generated magnetic fields (see, e.g., Rempel 2006, Karak & Choudhuri 2012, Passos et al. 2012).The meridional circulation at the bottom is also weakly correlated with the activity cycle (correlation coefficients between \hbox{$\mean B$}B and uθ(0.73 R⊙, ± 32°) ≈ −0.22, − 0.47). We see that \hbox{$\mean\Omega(0.95~R_\odot,\pm32^\circ)$}Ω(0.95R⊙,±32◦) (Fig. 13c) also shows a weak anti-correlation with the magnetic variations (having correlation coefficient ≈ − 0.25). The strong magnetic fields during maxima change \hbox{$\mean\Omega$}Ω by a few per cent (≈ 6%). However \hbox{$\mean\Omega(0.95~R_\odot,0^\circ)$}Ω(0.95R⊙,0◦) (Fig. 13d) shows positive correlation (correlation coefficient ≈ 0.36) and the overall variation is larger (≈ 12%). Because of this variation of \hbox{$\mean\Omega$}Ω at the equator, the values of \hbox{$\Delta_\Omega^{(r)}$}ΔΩ(r) and \hbox{$\Delta_\Omega^{(\theta)}$}ΔΩ(θ) (Figs. 13e, f) show a positive correlation with the magnetic field (correlation coefficients 0.36, 0.21) with the overall variation being ~ 75% and 166%, respectively.

2022AandA...659A.180G__Stangalini_et_al._2014_Instance_1

The reasons for studying the dynamic properties of small-scale magnetic fields in the quiet photosphere are manifold. Firstly, magnetic fields provide an opportunity to probe some aspects inherent to turbulent convection (see, e.g. Abramenko et al. 2011; Lepreti et al. 2012; Giannattasio et al. 2013, 2014a,b; Giannattasio et al. 2019; Abramenko 2014, 2017; Del Moro et al. 2015; Caroli et al. 2015; Chian et al. 2019; Giannattasio & Consolini 2021) and energy propagation to the upper atmospheric layers (see, e.g. Viticchié et al. 2006; Jefferies et al. 2006; Tomczyk et al. 2007; De Pontieu et al. 2007; Chae & Sakurai 2008; Stangalini et al. 2014, 2015, 2017; Stangalini 2014; Rouppe van der Voort et al. 2016; Gošić et al. 2018; Bellot Rubio & Orozco Suárez 2019; Rajaguru et al. 2019; Keys et al. 2019, 2020; Guevara Gómez et al. 2021; Jess et al. 2021) that cannot be addressed otherwise from an observational point of view. Secondly, they allow us to constrain the available energy in the quiet Sun and infer some details of the processes that amplify and organise them from subgranular to supergranular scales (see, e.g. November 1980; Roudier et al. 1998; Berrilli et al. 1999, 2002, 2004; Berrilli et al. 2005, 2013, 2014; Consolini et al. 1999; Getling & Brandt 2002; Rast 2002; Del Moro 2004; Del Moro et al. 2004; Getling 2006; Nesis et al. 2006; Centeno et al. 2007; Brandt & Getling 2008; de Wijn et al. 2008; Yelles Chaouche et al. 2011; Orozco Suárez et al. 2012; Giannattasio et al. 2018, 2020; Requerey et al. 2018). Thirdly, their study provides important constraints for theoretical models and/or models implemented in simulations of the photospheric layer (see, e.g. Stein & Nordlund 1998, 2001; Cattaneo et al. 2003; Vögler et al. 2005; Rempel et al. 2009; Shelyag et al. 2011; Beeck et al. 2012; Rempel 2014; Danilovic et al. 2015; Khomenko et al. 2017). All these aspects coalesce, advancing our knowledge of the processes capable of accumulating, transporting, and eventually dissipating the enormous amount of energy available in the photosphere of the quiet Sun.

2021MNRAS.504.5840F__Eriksen_et_al._2007_Instance_1

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).

2018MNRAS.478.3890B__Heckman_et_al._2017_Instance_1

Rather than AGN feedback, it is possible that the effects we are seeing are from a different process coeval or prior to the onset of AGN accretion. Several works have pointed out that AGN activity coincides with a recent starburst, with the AGN having significant accretion events at least ∼200 Myr after the starburst has occurred (Davies et al. 2007; Wild et al. 2007; Wild, Heckman & Charlot 2010; Yesuf et al. 2014) giving the neutral material time to propagate out to the impact parameters probed by COS-AGN (Heckman et al. 2017). With a sample of QSO sightlines probing the CGM around 17 low-redshift starburst and post-starburst galaxies, Heckman et al. (2017) have observed a similar signature of enhanced EWs of Ly α, Si iii, and C iv (the latter of which is not measured in our control sample) relative to a control-matched sample (matched in stellar mass and impact parameter). In the range of impact parameters and stellar masses probed by COS-AGN, the strength of our enhanced EW signature is consistent with the values probed by Heckman et al. (2017). However, the results of Heckman et al. (2017) show strong offsets in the kinematics of the gas from the host galaxy (≈100 km s−1; see fig. 5 from Heckman et al. 2017), whereas the COS-AGN sightlines do not (bottom panel of Fig. 6). Assuming that the AGN activity was triggered by the starburst, a minimum delay time of 200 Myr could allow for any starburst-driven winds to dissipate and kinematic offsets to no longer be present at the impact parameters of the COS-AGN sample. Although this starburst picture provides a possible explanation of our observations, we caution that starbursts are not the only astrophysical event linked to AGN accretion activity. For example, mergers that trigger the AGN (Ellison et al. 2011, 2013; Satyapal et al. 2014; Silverman et al. 2014; Goulding et al. 2018) could potentially affect the surrounding CGM gas. Past and future work focusing on the CGM of galaxy mergers can further test this result (Johnson et al. 2014; Hani et al. 2017; Bordoloi et al. in preparation).

2019ApJ...871...58T__Charbonnel_&_Lagarde_2010_Instance_2

We derived stellar parameters, [C/M], and [N/M] using SLAM. To avoid bad fits at the edges of the parameter space, we exclude stars with spectral S/N in the g band less than 50, and metallicity less than −1.4. The derived C and N abundances are shown in Figure 8. Clearly, in the top panel, the CH-strong, CH-normal, and metal-poor field stars are separated, and their relative distribution in the N–C parameter space is similar to the case of APOGEE abundances (left panel of Figure 7): (1) metal-poor field stars form a sequence in the lower left of the top panel. As evolved stars ascend the RGB, C and N abundances may be changed by first dredge-up (Iben 1964, 1967) and extra mixing (Gratton et al. 2000; Charbonnel & Lagarde 2010). Given that for a typical halo/thick-disk star of 1 M⊙, the first dredge-up occurs around Teff = 5200 K (Boothroyd & Sackmann 1999), and most of our sample stars have K and log g 2.5, we infer that most stars have already undergone first dredge-up. On the other hand, the C and N abundances of these stars could be altered by extra mixing. Stars with brighter K-band absolute magnitudes tend to have higher [N/Fe] and lower [C/Fe] (middle and bottom panels of Figure 8), which is consistent with extra-mixing theory and observation (Gratton et al. 2000; Charbonnel & Lagarde 2010); (2) CH-normal stars show an enhanced median N abundance and slightly depleted median C abundance. Clearly, the median N abundance of CH-normal stars is enhanced compared to that of normal metal-poor field stars with similar C abundances. In other words, the enhanced N abundances in CH-normal stars cannot be explained by the extra-mixing effect alone. We notice that a few CH-normal stars may have low N abundances, probably due to large uncertainties when spectra of a particular type are scarce in the training set, i.e., high-N metal-poor stars. The statistical similarity between APOGEE C and N abundances and LAMOST-derived C and N abundances further strengthens our statement above.

2016AandA...589A..44G__within_2000_Instance_2

W51e2 is the strongest and best-studied HC HII region in the W51 Main cluster, and it is believed to be powered by an O8-type young star (e.g., Shi et al. 2010a). A number of interferometric studies conducted with varying angular resolutions, at centimetre (cm) and (sub)millimetre (mm) bands, identified molecular and ionized gas undergoing infall and rotation toward W51e2. VLA observations of the NH3 inversion lines (1, 1) and (2, 2) seen in absorption (1\hbox{$\farcs$}.̋1 beamsize) revealed radial infall on scales larger than 5000 AU toward the W51e2 core (Zhang & Ho 1997). Higher angular resolution observations of the (3, 3) NH3 absorption line (0\hbox{$\farcs$}.̋3 beamsize) showed signatures of rotation within 2000 AU based on a position-velocity (pv) diagram (Zhang & Ho 1997). Zhang et al. (1998) identified a velocity gradient in a CH3CN transition at 2 mm, deriving a position angle (PA) of 20 ± 20°. Keto & Klaassen (2008) imaged the H53α radio recombination line (RL) with the VLA (0\hbox{$\farcs$}.̋45 beamsize) and they claimed rotation in the ionized gas along the axis of a molecular bipolar outflow (oriented NW-SE) imaged with the SMA in the CO (2−1) line (1′′ beamsize), suggesting a simple inflow/outflow picture in a single high-mass young stellar object (YSO). However, higher resolution observations, using the SMA at the wavelengths of 0.85 mm (0\hbox{$\farcs$}.̋3 beamsize) and 1.3 mm (0\hbox{$\farcs$}.̋7 beamsize), revealed a more complex picture, by resolving W51e2 into three subcores (Shi et al. 2010a): W51e2-W, corresponding to the HC HII region, W51e2-E, located about 1′′ east of the HC HII region and corresponding to the brightest dust continuum source, and W51e2-NW, the weakest continuum component, located about 1′′  NW of the HC HII region. Shi et al. (2010b) imaged the CO (3−2) line (with a 0\hbox{$\farcs$}.̋7 beamsize) and established that the driving source of the powerful molecular outflow in this region is the protostellar core W51e2-E, and not the HC HII region W51e2-W, challenging the scenario proposed by Keto & Klaassen (2008). Etoka et al. (2012) used MERLIN to image the Class II 6.7 GHz CH3OH masers (typical signpost of HMSF), and found that the bulk of maser emission is indeed concentrated toward W51e2-E, and not the HC HII region W51e2-W. This further supports the scenario proposed by Shi et al. (2010a), where the ongoing star formation activity in the region is not concentrated on the HC HII region but toward its companion 1′′ to the east.

2022AandA...658A..78S__Rosen_&_Krumholz_2020_Instance_1

Molecular outflows are a common and essential component in the formation process of low- and high-mass stars. In the past 40 yr, astronomers have mapped outflows in the whole mass range of young stellar objects (YSOs; e.g., Frank et al. 2014; Bally 2016; Anglada et al. 2018; Ray & Ferreira 2021). Magnetohydrodynamical (MHD) simulations have shown that the magnetic field plays a crucial role in the launching of molecular outflows (e.g., Pudritz & Ray 2019), more significantly so in the case of massive YSOs (e.g., Matsushita et al. 2018). Here, for instance, the presence of a magnetic field leads to the formation of early outflows. These reduce the radiation pressure, which allows the protostar mass to grow further (Banerjee & Pudritz 2007; Rosen & Krumholz 2020). In addition, the intensity of the magnetic field may influence the collimation of the outflows in massive YSOs. The outflows are well collimated for weak fields and poorly collimated for strong fields (Hennebelle et al. 2011; Seifried et al. 2012). In case of strong magnetic fields, the structure of the outflows is determined by the large-scale geometry of the magnetic field lines (Matsushita et al. 2017). Recently, Machida & Hosokawa (2020) have found a strong dependence of the evolution of outflows in massive YSOs on the initial magnetic field strength of the prestellar cloud for different accretion rates. In their 3D MHD simulations, they grouped the results into three categories: successful outflows, failed outflows, and delayed outflows. In the successful outflows, the outflows appear only when the prestellar cloud is strongly magnetized (μ1 = 2,3), and after an evolution time of ~104 yr, they reach a distance from the central protostar of about 104 au. When the magnetic field is weak (μ ≥ 5), we have failed and delayed outflows; even though small outflows of about 100–1000 au are observed in both cases, only in delayed outflows they can overcome the ram pressure and can ultimately grow. In a massive YSO, a large molecular outflow is therefore formed only if the initial magnetic field strength is B0 ≳B0,cr = 10−4(Mcl∕100M⊙) G, where Mcl is the cloud mass (Machida & Hosokawa 2020).

2020AandA...644L...7G__Magdis_et_al._2020_Instance_3

As in G18, we compiled existing constraints on the molecular gas fraction fgas of quiescent and pSB galaxies from recent literature, namely: local QGs consisting of the ATLAS3D (Young et al. 2011; Cappellari et al. 2013; Davis et al. 2014) and HRS (Boselli et al. 2014; Lianou et al. 2016) ETG samples as well as the samples of pSB galaxies (hereafter, the “low-z pSB” sample) of French et al. (2015) and Alatalo et al. (2016); at low and intermediate redshift, the ETG sample of Spilker et al. (2018) and the pSB sample of Suess et al. (2017); at intermediate and high redshift, constraints from Hayashi et al. (2018) on gas in z ∼ 1.46 cluster ETGs, as well as on individual galaxies from Sargent et al. (2015), Bezanson et al. (2019), and Rudnick et al. (2017). Given its size, we divided the ATLAS3D sample into high- and low-mass subsamples, choosing 5 × 1010 M⊙ as the cut-off mass. In addition, we also included fgas estimates derived from the (median) stacked FIR spectral energy distributions of ETGs at z ∼ 1.8 (G18; 977 galaxies), z ∼ 1.2, z ∼ 0.8, and z ∼ 0.5 (1394, 1536, and 563 galaxies, respectively; Magdis et al. 2020, hereafter M20). Finally, at higher redshift (z ∼ 3), we converted star formation rates (SFR) estimated from spectroscopy (Schreiber et al. 2018a; D’Eugenio et al. 2020) into gas masses assuming the star formation efficiency found by G18. As a consequence of our zmax = 3.5, we did not include higher-redshift quiescent galaxies (Glazebrook et al. 2017; Schreiber et al. 2018b; Tanaka et al. 2019; Valentino et al. 2020) in the analysis and considered z ∼ 3 galaxies as pSB. The dust-based estimates of G18 and M20 (and, by extension, the z ∼ 3 semi-constraints) assume a gas-to-dust ratio (G/D). It is dependent on metallicity, which is presumed to be solar or higher owing to both the relatively high gas-phase metallicity of MS galaxies at z ≲ 1 (e.g., Mannucci et al. 2010) and the already high stellar metallicities of QGs at z >  1 (Onodera et al. 2015; Estrada-Carpenter et al. 2019). Here we adopted an intermediate value between the solar and supersolar G/Ds used in M20, and we increased the error bars of these points to include both the solar and supersolar confidence estimates. These various samples, which are summarized with their selection criteria in Table B.1, combine into a nonhomogeneous dataset: some were specifically selected as ETGs, and others were based on varying degrees of quiescence. In particular, pSB galaxies are not necessarily truly quiescent and could, in principle, resume normal star formation. However, as a possible precursor of QGs, they provide useful, though not constraining (see Sect. 4), comparison samples for the model. Here we refer to all equally as either QGs or pSB galaxies, and we make the assumption that, on average, these different samples are not otherwise significantly biased with regard to their gas content compared to the full population, given each mass limit and type.

2018ApJ...853L..34B__Kóspál_et_al._2014_Instance_1

With the identification of the ∼6.6 day stellar rotation period, the question remains, what is origin of the ∼9.0 day signal in the periodogram? Because of K2’s large pixel size, we considered the possibility of contamination from additional sources in the field and found that images from DSS, Galex, 2MASS, WISE, and PanSTARRS show that there are no objects of comparable brightness within 1′ of CI Tau. We also examined the potential for multiplicity in the system. High-resolution observations reaching 5σ contrast at 025 separation (Uyama et al. 2017) provide no evidence for a companion down to ΔH = 6.8 that could contribute to the photometric signal. However, there is evidence in support of a substellar body. Johns-Krull et al. (2016) reported the detection of a planet orbiting CI Tau using data from an extensive optical and infrared RV survey. The planet mass they derived is Mp = 11.29 ± 2.16 MJup and the orbital period is Porb = 8.9891 ± 0.0202 days, consistent with the ∼9.0 day period shown in both of the Lomb–Scargle periodograms in Figure 1. Our current understanding of the CI Tau system is that the planet does not transit. This is supported by evidence that the disk is inclined i ∼ 45° (Guilloteau et al. 2014), though it is possible that a planet could be in an orbit misaligned with disk mid-plane (e.g., Kepler-63; Sanchis-Ojeda et al. 2013). However, the ∼9.0 day periodic signal may be the result of a planet–disk interaction because the presence of a massive planet in an actively accreting disk should show both spectroscopic and photometric variability. Indeed, Johns-Krull et al. (2016) find evidence in the Hα profile variations of CI Tau that the planet may be modulating the accretion of disk material onto the star. Although hotspots located at the foot of accretion streams can affect RV measurements mimicking the signal of an orbiting body (Kóspál et al. 2014; Sicilia-Aguilar et al. 2015), Johns-Krull et al. (2016) specifically looked for these signals and found no evidence that hotspots produced the RV signals seen in photospheric absorption lines. Given the stellar mass of 0.80 M⊙ (Guilloteau et al. 2014), Johns-Krull et al. (2016) determined a semimajor axis of 0.079 au for the planetary orbit. This would place the planet inside the inner edge of the disk at 0.12 au (McClure et al. 2013). It is probable that an 11.3 MJup planet so close to the inner edge of the disk would stimulate the accretion of material and possibly modify accretion onto the star, creating non-axisymmetric accretion flows (Tofflemire et al. 2017a, 2017b). As the planet orbits the star, this interaction could produce a periodic variation in the Hα emission, a tracer of accretion. The impact on stellar accretion could produce photometric variability on the ∼9.0 day period of the planet’s orbit, which K2 data show as a periodic component in the system brightness. Additionally, the strength of this periodic component can be expected to fluctuate because of the sporadic nature of accretion on short timescales (Herbst et al. 2007). This is observed in the analysis of the first and second halves of the data and is in contrast to the relatively consistent signal strength of the ∼6.6 day periodic component in brightness, which is caused by cold starspots that are long-lived in young stars (e.g., Stelzer et al. 2003) and produce relatively consistent fluctuations in brightness as the star rotates (Herbst et al. 2007; Bradshaw & Hartigan 2014). If the 9.0 day periodic signal is the result of stellar rotation, it would call into question the legitimacy of the planet. On the other hand, the similar RV amplitudes seen in the optical and the IR and the null results on tests for RV variations produced by an accretion hotspot (Johns-Krull et al. 2016) attest to the significance of planet’s detection. In addition, we would then need to explain the source of the 6.6 day signal, which is the most persistent signal observed in Figures 1 and 2.

2020MNRAS.499.4394M__Bate_et_al._2014_Instance_2

For these four remaining FHSC candidates (L1451-mm, MC35-mm, SM1N, and Oph A-N6) that have been observed at intermediate scales (few 100 au to few 1000 au) a final confirmation of their true evolutionary state requires higher resolution observations. For L1451-mm, the compact outflow needs to be resolved to investigate its morphology and kinematics, as a higher velocity component (an indication of protostellar nature) could be revealed by observations with a beam smaller than 100 au, similar to the case of B1b-N (Hirano 2019). An additional goal of high-resolution observations for L1451-mm and the remaining youngest candidates should be to investigate the temperature and density profiles of the envelope at scales from few au to 100 au. This is because simulations show that the temperature remains lower than ∼30 K even at several tens of au up to 100 au from the centre (Bate et al. 2014; Tomida et al. 2015; Hincelin et al. 2016; Young et al. 2019) during the FHSC stage. On the other hand, Class 0 sources show temperatures of 20–30 K or higher at scales of several 100 au (sufficient for thermal evaporation of CO) that results in the inner envelope and disc being easily detected using C18O observations (Yen et al. 2015, 2017; Stephens et al. 2018). This holds even in very low luminosity objects, for which the unexpected large extent of C18O is interpreted as evidence of a previous burst of accretion (Frimann et al. 2017; Hsieh et al. 2018). As for the density profile, simulations of the FHSC stage show a flat inner region, corresponding to the FHSC structure and extending up to ∼10 au (Tomida et al. 2013; Bate et al. 2014). For a protostar, on the other hand, the density profile should increase towards the central ∼1 au region (Young et al. 2019). Observations of the continuum emission with a resolution better than a few tens of au are likely required to model the emission and provide a density and temperature profile that can probe the relevant scales. Additional line observations with a similar resolution can also help to further distinguish between the different models. We note that, as pointed out in Young et al. (2019), distinguishing a dense core with only an FHSC and one that has recently formed a protostar but in which the FHSC structure is still present is likely not possible, even with high-resolution observations. Given the optically thick nature of the FHSC core, it is difficult to probe the physical properties within the FHSC structure. Despite this, finding a source with density and temperature profiles as well as with outflow properties consistent with the theoretical predictions will provide convincing evidence in support of a bona fide FHSC.

2017MNRAS.466.3961S__Grcevich_&_Putman_2009_Instance_1

Other important components of the X-ray sky are X-ray binaries, the hot gas present in our own Galaxy and the extragalactic hot gas. Amongst these X-ray sources, the large hot gas reservoir (Tvir ∼ 107 K) filling the space between the galaxies in the clusters, known as the ICM, has been observed in its X-ray emission for a long time (Reichert et al. 1981; Jones & Forman 1984; Branduardi-Raymont et al. 1985; Oukbir, Bartlett & Blanchard 1997; Diego et al. 2003a; Diego, Silk & Sliwa 2003b; Cavagnolo et al. 2009; Hurier et al. 2015). However, the hot gas (Tvir > 106 K) present in the form of circumgalactic medium (CGM) in massive galaxies (Mh ∼ 1012–1013 h−1 M⊙; Birnboim & Dekel 2003; Kereš et al. 2005; Singh et al. 2015) is less explored in X-rays due to its fainter X-ray emission. Some of the recent observations and studies (Grcevich & Putman 2009; Anderson & Bregman 2011; Dai et al. 2012; Putman, Peek & Joung 2012; Anderson, Bregman & Dai 2013; Bogdán et al. 2013a,b; Gatto et al. 2013) indicate that the CGM can account for a good fraction of the baryons in these galaxies. The X-ray emission from the CGM, therefore, is a promising tool to put strong constraints on the distribution and energetics of the gas (Singh et al. 2016) with eROSITA. At energies above 2 keV, the extragalactic point sources like AGN completely dominate the X-ray sky (Lehmann et al. 2001; Kim et al. 2007). Even below 2 keV, where the X-ray emission from the hot gas in the ICM and CGM is significant, the major contribution to the observed X-ray sky comes from AGNs (Sołtan 2007). Therefore, studying the X-ray emission from the AGN is crucial to understand the origin and evolution of the AGN as well as to extract the X-ray signal from the subdominant components. We thus also compute the angular power spectrum of the unresolved AGNs that are expected to contribute to the diffuse X-ray background of eROSITA and contaminate the angular power spectrum due the ICM/CGM in the 0.5–2 keV X-ray band.

2021MNRAS.508.4429C__Hobbs_et_al._2010_Instance_1

We explore another mechanism that can possibly result in torque reversals in the neutron star on long time-scales. It has been known for a long time that radio pulsars (which are usually isolated systems) show unexplained stochastic deviations in their spin-down behaviour (known as ‘timing noise’) on varied time-scales of a few hundred days to a few tens of years. This manifestation is akin to the random-walk behaviour in spin frequency observed in wind-fed accretion powered pulsars like Vela X-1. In an interesting study of timing irregularities of a sample of 366 pulsars, Hobbs, Lyne & Kramer (2010) found some radio pulsars showing quasiperiodic structures in their long-term timing residuals. From power spectrum analysis, significant periodicities ranging from about 1.4 to 10 yr were found in PSR B1540−06, PSR B1642−03, PSR B1818−04, PSR B1826−17, PSR B1828−11, and PSR B2148+63 (Hobbs et al. 2010). Interestingly, we also find quasiperiodic variations in the long-term spin evolution of Vela X-1 on time-scales of about 5.9 yr that is comparable to those inferred for radio pulsars showing quasiperiodic changes in their timing residuals. The underlying phenomena causing quasiperiodic structures in timing noise of radio pulsars is elusive. However, it has been suggested that these changes are driven by changes in the magnetosphere of the neutron star (Lyne et al. 2010). In this ‘state-switching model’, the magnetosphere of the neutron star is suggested to harbour two or more magnetospheric states which can be stable on time-scales of years but the pulsar can switch abruptly between these states driven by changes in the parameters regulating the spin-down (Lyne et al. 2010). This can possibly happen in Vela X-1 where the coupling between the dynamic magnetosphere and the neutron star can change in a quasiperiodic fashion. Interestingly, the disc-fed pulsar LMC X-4 has been found showing a near cyclic spin period evolution on time-scales of about 6.8 yr (Molkov et al. 2016) which is within a factor of 1.2 of the inferred time-scale in wind-fed pulsar Vela X-1. Recent observations of transient pulsar V0332+53 suggests switching of coupling between the accretion disc and the neutron star magnetosphere in a disc-fed pulsar (Doroshenko et al. 2017).

2016AandA...592A..19C__Maraston_et_al._(2009)_Instance_2

Since the star-formation histories of galaxies (ETGs included, e.g. De Lucia et al. 2006; Maraston et al. 2009) can be stochastic and include multiple bursts, we also verify the full-spectrum fitting capabilities to retrieve more complex SFHs. In particular, we take an 11 Gyr old composite stellar population with an exponentially delayed SF (τ = 0.3 Gyr) as the main SF episode (this age is compatible with the age of the Universe at z ~ 0.15, which is the median redshift of our sample, see Sect. 3). We then define more complex SFHs by combining this single CSP with a burst of SF at different ages (5, 6, 7 Gyr) and with different mass contributions (3, 5, 10 %). In all cases, we consider a solar metallicity for the main SF episode and, according to the results of Maraston et al. (2009), a subsolar metallicity (Z = 0.004) for the later one. We do not mask any spectral feature of the input spectra, we assume AV = 0.1 mag for the two components and apply a velocity dispersion of 200 km s-1. We show the results for a S/N of 80, which matches the typical S/N of the SDSS median stacked spectra analyzed in the following (see Sect. 3). Useful information can be derived from the comparison between the output SFH obtained from these input simulated spectra and the one provided when the single CSP alone is taken as input SFH. Fig. 5 shows that the single CSP alone is well recovered by the full spectrum fitting. In particular, ~80% of the stellar mass is retrieved within ~1 Gyr from the SFH peak. When a burst is added to this major episode of SF, the full-spectrum fitting is able to recognize the presence of a more complex SFH, as indicated by the tail appearing at smaller ages, and the total mass percentage of the later burst is retrieved within 1 Gyr from the expected age. However, we note that the main episode of SF is spread on a time interval longer than expected, and 50% of the stellar mass is retrieved around ~1 Gyr from the SFR peak. We also find that, in this case, the mean properties of the global stellar population are well retrieved, with a percentage accuracy larger than 10% starting from S/N ~ 15 for age, ~7 for metallicity, ~20 for AV, ~8 for σ and that the metallicities of the two SF episodes are separately recovered. These S/Ns are well below those typical of the stacked spectra analyzed in the following sections.

2016ApJ...819...97T__Gordon_et_al._1987_Instance_1

The ReTOF results from irradiated phosphine and deuterated methane ice provide crucial information regarding the mechanism of formation for methylphosphanes by analyzing the intensities of various isotopologues. Figure 8 shows the possible formation routes that would lead to each of the three observed isotopologues of methylphosphine (CH3PH2). To obtain m/z = 50 (CHD2PH2), CD4 has to decompose via the loss of molecular hydrogen or two deuterium atoms to form carbene (CD2), which has been observed in previous irradiated ice studies (Holtom et al. 2005; Bennett & Kaiser 2007), and then insert into a phosphorus–hydrogen bond of phosphine (reaction (5)). If the carbene is formed in its first excited singlet state (a1A1), the insertion is barrierless (Gordon et al. 1987). For m/z = 51 (CD3PH2), methane and phosphine each lost a hydrogen or deuterium atom, and the resulting methyl (CD3) (Kaiser et al. 1997) and phosphino (PH2) radicals recombined barrierlessly (reaction (6)). Finally, the formation of m/z = 52 (CD3PHD) mirrors that for CHD2PH2 but in this case phosphine lost two hydrogen atoms or molecular hydrogen to create the phosphinidene (PH) radical and then inserted into a carbon–deuterium bond of methane (reaction (7)). Phosphinidene, like imidogen (NH) (Fueno et al. 1983), is expected to insert barrierlessly in its first excited singlet state (a1Δ): 5a 5b 6a 6b 6c 7a 7b Therefore, our results provide compelling evidence that methane decomposes not only to the methyl radical, but also to carbene. Likewise, phosphine was found to fragment to the phosphino radical and also to phosphinidene. The ratio of ion intensities for m/z = 50:51:52 is 2:10:1, indicating that radical recombination was the preferred formation pathway with CD3PH2 as the most abundant isotopologue. This could either be a result of the methyl and phosphino radicals reacting quickly or that more of these radicals were produced than carbene and phosphinidene.

2021ApJ...911...59L__Kereš_et_al._2005_Instance_1

In addition to the galaxy colors (e.g., Baldry et al. 2004; Bell et al. 2004; Borch et al. 2006; Xue et al. 2010; Salim 2014; Lee et al. 2015; Wang et al. 2017), there are other indicators characterizing the star formation nature of galaxies, e.g., the morphology (Strateva et al. 2001; Barro et al. 2013, 2014; Pan et al. 2013) and some spectral features such as the Balmer absorption line (e.g., Kuntschner et al. 2002; Kim et al. 2013; Kim & Yoon 2017) and the 4000 Å break (e.g., Kauffmann et al. 2003; Lambas et al. 2012; Rowlands et al. 2018; Angthopo et al. 2019). Using these indicators, a number of studies have found that the color or SFR transition of galaxies is associated with the consumption of their gas content (e.g., Kereš et al. 2005; Dekel & Birnboim 2006; Kruijssen 2015; Nelson et al. 2018). If this “quenching” scenario is correct, the timescales of the galaxy color/SFR transition and their gas depletion must be consistent. Some authors have studied galaxy quenching by quantifying the evolution of SMFs (or luminosity functions) of SFGs and QGs at high redshifts (e.g., Fritz et al. 2014; Rowlands et al. 2018). The others, with the help of some infrared or submillimeter surveys (e.g., Hershel and ALMA), have managed to study the cosmic evolution of the gas content as well as the gas-depletion rate of galaxies with different SFRs (e.g., Geach et al. 2011; Lagos et al. 2011; Tacconi et al. 2018; Liu et al. 2019; Castignani et al. 2020; Magnelli et al. 2020). These studies have revealed that both the galaxy quenching and gas-depletion timescales increase with decreasing redshifts. The typical quenching timescale of GV galaxies can be as long as several billion years in the local universe (Rowlands et al. 2018; Correa et al. 2019; Phillipps et al. 2019). Methods such as fitting the spectral energy distributions (SEDs) of galaxies (e.g., Belfiore et al. 2018; Phillipps et al. 2019; Zick et al. 2018) and using cosmological simulations (e.g., Feldmann et al. 2017; Nelson et al. 2018; Correa et al. 2019; Donnari et al. 2019) have been widely used to derive the star formation histories of galaxies and their lifetimes.

2017MNRAS.469.3738S__Wargelin_etal._2004_Instance_1

For the spectral fitting, the astrophysical background components are determined from a simultaneous fit to data from the RASS4 (Snowden etal. 1997). The extraction region for the RASS data is an annulus from 0.7 to 1 around the cluster centre (for NGC4636, NGC1399 and A3526 r500 is larger than 0.7, so the RASS data were extracted from 1.5 to 2). The particle background was directly subtracted from the Chandra spectra using the stow events files from an epoch close to the observation date. The stow events files are created when the ACIS detector is in a position where it is not exposed to the sky and the HRC-I camera is in the field of view. This configuration is also called event histogram mode. As shown by comparisons to dark moon observations only particle events are recorded in the stow position (Markevitch etal. 2003; Wargelin etal. 2004). For each annulus, the same detector region was used to extract the particle background spectra. These background spectra are normalized by the ratio of the (9.512)keV band count rate of the observation and the stow events file to account for variations of the quiescent particle background component. The cluster emission is modelled by an absorbed thermal model (phabs*apec), where all parameters apart from the redshift and the NH are left free to vary. Following Willingale etal. (2013), the hydrogen columns density used as a tracer for the X-ray absorption, (16) \begin{eqnarray} N_\mathrm{H\, tot} = N_\mathrm{H\,\small {I}} + 2\cdot N_{\mathrm{H}_2\mathrm{m}} \cdot \left[ 1-\exp \left(-N_\mathrm{{\rm H\,\small {I}}}\cdot \frac{ E\left(B-V\right) }{ N_\mathrm{c}} \right) \right]^\alpha , \nonumber\\ \end{eqnarray} where the parameters NH2m 7.2 0.3 1020cm 2, Nc3.0 0.31020cm2 and 1.1 0.1 were calibrated using X-ray afterglows of Gamma-ray bursts in the aforementioned reference. Both, the absorption E(BV) from the IRAS and COBE/DIRBE infrared dust maps (Schlegel, Finkbeiner Davis 1998) and the $N_\mathrm{H\,\small {I}}$ from Kalberla etal. (2005) are computed at each cluster position. The combined effect of the uncertainties of these parameters, the scatter of this scaling relation (0.087) plus accounting for a 10percent uncertainty on $N_\mathrm{H\,\small {I}}$ and E(BV) has only an 11percent effect on NHtot, which typically affects best-fitting temperatures by 1percent. Since this is much smaller than the typical statistical uncertainties, any statistical uncertainty of NHtot is neglected. For the relative abundance of heavy elements the Asplund etal. (2009) abundance table was used. For each observation, all spectra from the different regions and chips5 are fit simultaneously. The temperature and metallicity of spectra from the same region but different chips are linked together, while the normalizations are not because of spatial variations of the density distribution. For all observations, the steppar command was run on the temperatures. This task calculates the 2 for the parameter within a given range of values in order not to get best-fitting parameters of a local minimum of the likelihood distribution. The reduced 2 of all spectral fits was on average 1.03, while in 95percent of the cases it was below 1.17. This gives a hint that the spectral modelling is appropriate.

2020ApJ...894..107I__Gibb_et_al._2004_Instance_1

AFGL 2136 IRS 1 (also referred to as CRL 2136, G17.64+0.16, and IRAS 18196−1331) is a luminous (1.0 × 105 L; Lumsden et al. 2013), high-mass (45 ± 10 M; Maud et al. 2019) protostar that is believed to be in the latter stages of its evolution due to a variety of observed characteristics (Boonman & van Dishoeck 2003; Maud et al. 2018 and references therein). It is located at a distance of 2.2 kpc away from the Sun (Urquhart et al. 2014), and has been extensively observed from centimeter to micron wavelengths, at low and high angular resolution, and low and high spectral resolution. The myriad observations paint a picture where a single, isolated massive protostar is driving a wide-angle bipolar outflow through its natal cloud. The large scale outflow is observed in CO emission at millimeter wavelengths, with both the red and blue lobes being about 100″ in extent (Kastner et al. 1994; Maud et al. 2018). Closer to the central source (2″–10″), the outflow cavity walls are seen in scattered light at near-infrared wavelengths (Kastner et al. 1992; Murakawa et al. 2008; Maud et al. 2018). The cool molecular envelope exhibits ice and dust absorption bands (Willner et al. 1982; Keane et al. 2001b; Dartois et al. 2002; Gibb et al. 2004), as well as molecular emission at millimeter wavelengths (van der Tak et al. 2000a, 2000b), but a much warmer component is also inferred from several different molecules seen in absorption in the near- to mid-infrared (Mitchell et al. 1990; Lahuis & van Dishoeck 2000; Keane et al. 2001a; Boonman et al. 2003; Boonman & van Dishoeck 2003; Goto et al. 2013, 2019; Indriolo et al. 2013a). The presence of a dust disk on small spatial scales was suggested by near-infrared polarization imaging (Minchin et al. 1991; Murakawa et al. 2008) and by mid-infrared interferometric observations (de Wit et al. 2011; Boley et al. 2013). A compact source was marginally resolved at centimeter wavelengths, along with a cluster of nearby 22 GHz H2O masers (Menten & van der Tak 2004), but only with the recent ALMA 1.3 mm continuum observations has the 93 × 71 mas dust disk been fully resolved (Maud et al. 2019). Thermal line emission at 232.687 GHz from the H2O ν2 = 1–1, 55,0–64,3 transition has the same spatial extent as the dust emission, and the H2O gas velocities indicate Keplerian rotation within the disk (Maud et al. 2019). It is ideal that the reader has a clear picture of the AFGL 2136 region in mind to best understand the discussion throughout this paper. In particular, Figure 10 of Maud et al. (2018) provides an up-to-date schematic diagram of the AFGL 2136 region, and Figures 1 and 2 of Maud et al. (2019) present the compact disk observed in dust and gas emission, respectively.

2018MNRAS.476.1412I__Yuan,_Quataert_&_Narayan_2003_Instance_1

There have been a number of analytical solutions and numerical studies for rotating flows with viscous angular momentum transport (e.g. Shakura & Sunyaev 1973; see reviews by Pringle 1981; Kato, Fukue & Mineshige 2008, references therein). Among those, we here focus on accretion flows which cannot lose internal energy via radiative cooling because of very low gas density. Such radiatively inefficient accretion flows are quite interesting since many observed BHs accrete at rates of only a small fraction of the Bondi accretion rate and their radiation luminosity is as low as ∼10−1 to 10−9 LEdd (Ho 2008, 2009). Sagittarius A* (Sgr A*) is inferred to be accreting at a rate of 10−3 to $10^{-2}\ \dot{M}_{\rm B}$ (e.g. Yuan, Quataert & Narayan 2003; Quataert 2004), where $\dot{M}_{\rm B}\simeq 10^{-5}\ {\rm M}_{\odot }\, {\rm yr}^{-1}$ is measured from the temperature and density near the Bondi radius with X-ray observations (Baganoff et al. 2003). Because of such a low accretion rate, the bolometric luminosity of Sgr A* (M• ≃ 4 × 106 M⊙; Ghez et al. 2003) is as small as Lbol ∼ 1036 erg s−1 ∼ 2 × 10−9 LEdd. The second example is a BH at the centre of the giant elliptical galaxy M87. The gas accretion rate at the vicinity of the BH is estimated as ≲ 9.2 × 10− 4 M⊙ yr− 1 (Kuo et al. 2014), which is lower than ${\sim } 10^{-2}\ \dot{M}_{\rm B}$ (Russell et al. 2015). Since the BH mass is estimated as $M_\bullet = 6.6^{+0.4}_{-0.4}\times 10^9\ {\rm M}_{\odot }$ (Gebhardt et al. 2011) and $M_\bullet = 3.5^{0.9}_{-0.7}\times 10^9\ {\rm M}_{\odot }$ (Walsh et al. 2013), the bolometric luminosity of Lbol ∼ 2 × 1041 erg s−1 is ∼3 × 10−7 LEdd. The third example is a BH at the centre of the Andromeda Galaxy (M31). The estimated BH mass is $M_\bullet \simeq 1.4^{+0.7}_{-0.3}\times 10^8\ {\rm M}_{\odot }$ (Bender et al. 2005). The Bondi accretion rate and the X-ray luminosity are estimated as $\dot{M}_{\rm B}\simeq 7\times 10^{-5}\ {\rm M}_{\odot }\, {\rm yr}^{-1}$ and LX ≃ 2 × 1036 erg s−1 ≃ 10−10 LEdd, respectively (Garcia et al. 2010). The corresponding bolometric luminosity is inferred as ≃10−9 LEdd by assuming the bolometric correction factor to be ≃10 (Hopkins, Richards & Hernquist 2007).

2021AandA...654A.124W__Tanvir_et_al._2017_Instance_2

The first multi-messenger GW event was discovered on 17 August, 2017. About 1.7 s after the GW170817 signal detected by LIGO and Virgo (Abbott et al. 2017a), the Fermi Gamma-ray Burst Monitor was successfully triggered by GRB 170817A (Abbott et al. 2017b; Goldstein et al. 2017; Zhang et al. 2018) and subsequently a large number of follow-up observations monitored the afterglow emission in different electromagnetic bands from the radio to X-rays (Alexander et al. 2017; Hallinan et al. 2017; Margutti et al. 2017; Troja et al. 2017; D’Avanzo et al. 2018; Ghirlanda et al. 2019; Lazzati et al. 2018; Lyman et al. 2018) and the kilonova AT 2017gfo in the ultraviolet–optical–infrared band (Abbott et al. 2017c; Andreoni et al. 2017; Arcavi et al. 2017; Chornock et al. 2017; Coulter et al. 2017; Covino et al. 2017; Cowperthwaite et al. 2017; Evans et al. 2017; Hu et al. 2017; Kilpatrick et al. 2017; Lipunov et al. 2017; Nicholl et al. 2017; Smartt et al. 2017; Soares-Santos et al. 2017; Tanvir et al. 2017). The observations of GRB 170817A and its afterglows robustly confirmed the long-standing hypothesis that SGRBs can originate from compact binary mergers. Moreover, it became possible to explore the angular structure of the SGRB jet from an off-axis view (Lamb & Kobayashi 2017; Granot et al. 2018; Lazzati et al. 2018; Mooley et al. 2018a,b; Li et al. 2019). Meanwhile, the observations of AT 2017gfo indicated the existence of the merger ejecta, which suggests that the progenitor binary should at least contain one NS. In more detail, the existence of a “blue” and possibly also a “purple” component in the AT 2017gfo emission further indicated that the merger product of the GW170817 event is very likely to be a hypermassive NS, which lasted for at least a few hundred milliseconds, as an immediately formed black hole can only be associated with a “red” kilonova1 (Cowperthwaite et al. 2017; Perego et al. 2017; Tanaka et al. 2017; Tanvir et al. 2017; Villar et al. 2017; Kawaguchi et al. 2018). Therefore, in summary, the progenitor of the GW170817 event can be identified as a DNS system, which is consistent with the result of the GW analysis.

2022MNRAS.509.1010R__Laughlin_&_Adams_1998_Instance_1

Recent work by Longmore, Chevance & Kruijssen (2021) has revealed an intriguing correlation between stellar phase-space density and the architecture of planetary systems, in particular the multiplicity. This work followed a similar analysis by Winter et al. (2020), which uncovered a correlation between stellar phase-space density and the occurrence of hot Jupiters. Using Gaia DR2 data (Gaia Collaboration et al. 2018), Longmore et al. (2021) computed the local stellar phase-space density of planet-hosting stars and their neighbours (within 40 pc) to determine whether the exoplanet host was in a relatively low or high phase-space density zone compared to its neighbours. They hypothesized that stars in current stellar overdensities were previously part of dense stellar clusters, from which only local residual overdensities remain. They showed that Kepler systems in local stellar phase-space overdensities have a significantly larger single-to-multiple ratio compared to those in the low phase-space density environment. The origin of this correlation is puzzling, as stellar clustering is expected to affect mostly the outer part of planetary systems in very dense environments (Laughlin & Adams 1998; Malmberg, Davies & Heggie 2011; Parker & Quanz 2012; Cai et al. 2017; Li, Mustill & Davies 2020a). Recent works have also suggested that the correlation is weaker when using a smaller unbiased stellar sample (Adibekyan et al. 2021), and that the current stellar overdensities could be associated with stellar age or to galactic-scale ripples as opposed to dense birth clusters (Mustill, Lambrechts & Davies 2021; Kruijssen et al. 2021). Alternatively, new studies have suggested that stellar flybys can excite the eccentricities and inclinations of outer planets/companions, which then trigger the formation of hot Jupiters from cold Jupiters via high-eccentricity migration (Wang et al. 2020; Rodet, Su & Lai 2021). For this flyby scenario to be effective, certain requirements (derived analytically in Rodet et al. 2021) on the companion property (mass and semimajor axis) and the cluster property (such as stellar density and age) must be satisfied. In this paper, we will examine a similar ‘outside–in’ effect of stellar flybys on the SE systems. Earlier, Zakamska & Tremaine (2004) examined the excitation and inward propagation of eccentricity disturbances in planetary systems. Our work focuses on inclination disturbances, as they are most relevant in determining the co-transit geometry of multiplanet systems.

2015MNRAS.448.1644S__Rines_&_Diaferio_2006_Instance_1

Despite a number of attempts of employing aspherical models for the matter distribution in the analysis of X-ray and lensing observations of galaxy clusters (see e.g. Corless, King & Clowe 2009; Samsing, Skielboe & Hansen 2012; Sereno et al. 2013), all dynamical methods are based on spherical symmetry. As the first step in addressing the problem of asphericity in mass measurements based on kinematics of galaxies in clusters, we study how dynamical mass estimators assuming spherical symmetry depend on the orientation of galaxy clusters with respect to the line of sight. We assess this effect by studying dynamical masses inferred from mock kinematic data of galaxy clusters generated from cosmological simulations. We restrict our analysis to dynamical masses measured with the so-called caustic technique (Diaferio 1999), which is one of the commonly used methods of mass determination in galaxy clusters (see e.g. Biviano & Girardi 2003; Rines & Diaferio 2006; Lemze et al. 2009; Geller et al. 2013; Rines et al. 2013). The caustic technique does not explicitly assume dynamical equilibrium beyond the virial radius, therefore it can be used to measure masses of galaxy clusters at distances larger than their virial radius and allows us to study the mass bias in a wide range of radii. As all dynamical methods currently applied to cluster data, it assumes spherical symmetry and testing this assumption is an objective of this work. Dependence of the mass measurement on the orientation of galaxy clusters with respect to the sight line is expected not only due to clusters’ intrinsic phase-space shapes, but also due to co-alignment of the surrounding large-scale structures. The latter effect has been clearly shown both in cosmological simulations (Libeskind et al. 2013) and observations (Paz et al. 2011), and it is expected to modulate the contribution of background galaxies in kinematic samples and thus to affect the final estimate of dynamical mass.

2015AandA...576A...5C__Jørgensen_et_al._2012_Instance_3

The relative abundances of the three species are derived from the column densities in Table 2 and are compared with other star-forming regions and comets in Table 3. The (CH2OH)2/CH2OHCHO abundance ratio of ~0.3–0.5 previously derived in IRAS 16293 by Jørgensen et al. (2012) was revised. Indeed, the assignment in Jørgensen et al. (2012) was based on only one line of the gGg′ conformer of ethylene glycol about 200 cm-1 (~290 K, Müller & Christen 2004) above the lowest-energy aGg′ conformer – and thus tentative. An analysis from observations of six transitions of the lower energy conformer from ALMA Cycle 1 observations at 3 mm (four spectral windows at 89.48–89.73, 92.77–93.03, 102.48–102.73, and 103.18–103.42 GHz; Jørgensen et al., in prep.) results in a higher ethylene glycol-to-glycolaldehyde abundance ratio of 1.0 ± 0.3. This new estimate is consistent with the ratio expected between the aGg′ and gGg′ conformers under thermal equilibrium conditions at 300 K, the excitation temperature of glycolaldehyde derived in IRAS 16293 (Jørgensen et al. 2012). The (CH2OH)2/CH2OHCHO abundance ratio in IRAS2A is estimated at 5.5 ± 1.0 if we consider the column densities derived from the rotational diagrams. It is slightly lower (4.6), however, if we use the column density of ethylene glycol of 1.1 × 1016 cm-2 that does not overproduce the peak intensities of a few lines (see Fig. 3). The (CH2OH)2/CH2OHCHO abundance ratio consequently is a factor ~5 higher than in the Class 0 protostar IRAS 16293. It is also higher than in the other star-forming regions (see Table 3), but similar to the lower limits derived in comets (≳3–6). This indicates that the glycolaldehyde chemistry may in general vary among hot corinos. It is possible that like IRAS2A, other very young low-mass protostars show high (CH2OH)2/CH2OHCHO abundance ratios, in agreement with the cometary values. The CH3OCHO/CH2OHCHO column density ratio found in IRAS2A (~20) ranges between the values derived in the molecular clouds from the Galactic center (~3.3–5.2) and the high-mass star-forming regions (~40–52). A lower limit of 2 was derived for comet Hale-Bopp.

2016MNRAS.462.3441D__Namouni_1999_Instance_2

In principle, Fig. 5, central panel G, shows that (469219) 2016 HO3 may have been locked in a Kozai–Lidov resonance with ω librating about 270° for nearly 100 kyr and probably more. Because of the Kozai–Lidov resonance, both e (central panel E) and i (central panel F) oscillate with the same frequency but out of phase (for a more detailed view, see Fig. 4, panels E and F); when the value of e reaches its maximum the value of i is the lowest and vice versa ($\sqrt{1 - e^2} \cos i \sim$ constant, see Fig. 4, panel B). During the simulated time and for the nominal orbit, 469219 reaches perihelion and aphelion the farthest possible from the ecliptic. Fig. 5, G-panels, show that for other incarnations of the orbit of 469219, different from the nominal one, ω may librate about 90° as well during the simulated time interval. However, is this a true Kozai–Lidov resonance? Namouni (1999) has shown that the secular evolution of co-orbital objects is viewed more naturally in the erωr-plane, where er and ωr are the relative eccentricity and argument of perihelion computed as defined in Namouni's work (see equations 3 in Namouni 1999); these are based on the vector eccentricity and the vector inclination. Fig. 6 shows the multi-planet erωr-portrait for the nominal orbit of this object. It clearly resembles figs 13 and 19 in Namouni (1999). Asteroid 469219 librates around $\omega _{\rm r}=-90{^\circ }$ for Venus, the Earth, and Jupiter. This behaviour corresponds to domain III in Namouni (1999), horseshoe-retrograde satellite orbit transitions and librations (around $\omega _{\rm r}=-90{^\circ }$ or 90°). For a given cycle, the lower part corresponds to the horseshoe phase and the upper part to the quasi-satellite or retrograde satellite phase. This is not the Kozai–Lidov resonance; in this case, the Kozai–Lidov domain (domain II in Namouni 1999) is characterized by libration around $\omega _{\rm r}=0{^\circ }$ (or 180°) which is only briefly observed at the end of the backwards integrations (see Fig. 6). The Kozai–Lidov resonance is however in action at some stage in the orbits displayed in Figs 5 and 8. Our calculations show that the orbital evolution followed by 469219 is the result of the dominant secular perturbation of Jupiter as the periodic switching between co-orbital states ceases after about 8 kyr if Jupiter is removed from the calculations. Fig. 7 shows that, without Jupiter, 469219 switches between the Kozai–Lidov domain and that of horseshoe-quasi-satellite orbit transitions and librations (including both −90°and 90°). Jupiter plays a stabilizing role in the dynamics of objects following orbits similar to that of 469219. It is not surprising that Jupiter instead of the Earth or Venus is acting as main secular perturber of 469219. Ito & Tanikawa (1999) have shown that the inner planets share the effect of the secular perturbation from Jupiter; in fact, Venus and our planet exchange angular momentum (Ito & Tanikawa 2002). In their work, these authors argue that the inner planets maintain their stability by sharing and weakening the secular perturbation from Jupiter. Tanikawa & Ito (2007) have extended this analysis to conclude that, regarding the secular perturbation from Jupiter, the terrestrial planets form a collection of loosely connected mutually dynamically dependent massive objects. The existence of such planetary grouping has direct implications on the dynamical situation studied here; if Jupiter is removed from the calculations, the overlapping secular resonances and the recurrent dynamics disappear as well.

2016ApJ...832...57P__Parashar_et_al._2009_Instance_1

We employ two types of kinetic codes, hybrid particle-in-cell (PIC) and full PIC simulations. Both types make use of the P3D family of codes (Zeiler et al. 2002), in hybrid PIC (e.g., Parashar et al. 2011) mode, and fully kinetic PIC mode (e.g., Wu et al. 2013b). All simulations discussed here are performed in the 2.5D geometry (two-dimensional (2D) grid and all three components of field vectors). The hybrid simulation has (where is the ion inertial length, with c the speed of light and the proton plasma frequency), , 200 particles per cell, , cold isothermal electrons with . The simulation is initialized with energy only in wavevectors that have . v and b fluctuations are chosen with a specified initial spectral shape, Gaussian random phases, and only in essentially incompressive modes of the system. This simulation was also used in a recent study of variance anisotropy in kinetic plasmas (Parashar et al. 2016). The first full PIC simulation has , , 200 particles per cell, , . The initial condition is the Orszag–Tang vortex (OTV) (e.g., Orszag & Tang 1979; Dahlburg & Picone 1989; Parashar et al. 2009; Vasquez & Markovskii 2012). This simulation was performed for a recent study of transition from kinetic to MHD-like behavior (Parashar et al. 2015). The final PIC simulation (Turb812) has , , 400 particles per cell, , . The initial condition is MHD-like, and more “turbulent,” with v and b fluctuations excited in a band of wave-vectors with with a specified initial spectrum. This simulation was done as part of a recent study that discussed the relation of timescales at the proton gyroscale and their relation to relative proton–electron heating (Matthaeus et al. 2016). PIC codes have an inherent noise associated with them due to the finite number of particles per cell. While performing these simulations, the two most important numerical criteria that we paid attention to were: (i) excellent conservation of total energy (less than a few percent change in any fluctuation energy), and (ii) the particle noise in the spectrum was significant only at scales much smaller than the scales of interest (Debye length for PIC and di for hybrid PIC). On this basis, the modest number of particles employed here was considered adequate. As an additional measure, we employed filtering (e.g., Wan et al. 2012) to remove particle noise at grid scales prior to computing gradients (e.g., vorticity).

2022AandA...662A..42M__Vázquez_2007_Instance_2

A number of fundamental results have been rigorously proved in the mathematical literature concerning the asymptotic behaviour in time of some of the solutions of the porous medium equation and related equations (e.g. Kamin & Vázquez 1991; Bernis et al. 1993; Hulshof et al. 2001). What is of interest for us here is, primarily, the results that can be applied to the cylindrically symmetric case with diffusion coefficient which is proportional to the square of the dependent variable (n = 2, m = 3 in the notation of Eq. (7)). The most basic result, already mentioned in Sect. 4.4.1, is that initial conditions which have a finite nonzero flux integral (called ‘the mass’ in the mathematical literature for the PME) converge toward the ZKBP solution with the same flux integral (‘mass’) asymptotically in time (Vázquez 2007, Theorem 18.2); here, allowance is made for either a positive or negative flux integral by globally changing the sign of the ZKBP solution; also, ‘convergence’ is meant in the sense that the Lp norm of the difference between the actual solution and the ZKBP function tends to zero as t → ∞ faster than a negative power of the time with an exponent which is a function of n, m, and p (e.g. −1/3 for n = 2 and m = 3 in the L2 norm; see details in the book by Vázquez 2007). A complementary result is the following: when the initial condition has positive net flux and its negative part has compact support, then the whole solution evolves into a positive function after a finite time (Vázquez 2007, Theorem 18.29). Since we are dealing with signed functions which have zero flux integral, these results are of interest mainly because they impose a strict condition on the possible flux imbalance caused by numerical errors (as discussed in Sect. 4.4.1, final paragraph): if it is not small, the numerical solutions will approach the ZKBP solution in a comparatively short time. However, the flux imbalance in all the Bifrost experiments discussed in the present paper is small enough that they have not shown this behaviour even though they have been run until a very long diffusive time.

2022MNRAS.517.5744G__Caro_et_al._2016_Instance_1

The CO photodesorption yield reaches its highest value when this ice is deposited at low temperatures (down to 7 K, the lowest temperature studied experimentally) and decreases gradually at higher deposition temperatures (Öberg et al. 2007; Öberg et al. 2009; Muñoz Caro et al. 2010, 2016; Sie et al. 2022). The explanation for this phenomenon motivated further research. It was found that the columnar structure of CO ice samples, grown at incidence angles larger than 45°, increases the effective ice surface exposed to UV photons and therefore the photodesorption efficiency (González Díaz et al. 2019), but ice surface effects cannot account for the large variations observed in the photodesorption of CO ice samples deposited at different temperatures (Muñoz Caro et al. 2016). Absorption band shifts of CO ice in the UV and IR ranges only occurred at deposition temperatures above 20 K (Lasne et al. 2015; Muñoz Caro et al. 2016), suggesting that CO ice grown at lower temperatures is amorphous below 20 K in our experiments, and therefore, the decreasing photodesorption yield is not related to a transition from amorphous to crystalline ice, instead it might be associated to a different degree of molecular disorder in CO ice samples, depending on their deposition temperature. Photon energy transfer via Wannier-Mott excitons between the first photoexcited molecule in the ice and a molecule on the ice surface capable to desorb was proposed (Chen et al. 2017; McCoustra & Thrower 2018). Molecular disorder seems to enhance this energy transfer between neighbour molecules. The colour temperature variations measured at different deposition temperatures could also be the result of molecular disorder (Carrascosa et al. 2021). Urso et al. (2016), Cazaux et al. (2017), and Carrascosa et al. (2021) did not find significant changes in the desorption behaviour or the colour temperature of pure CO ice during controlled warm-up, which points to a low value of the diffusion in the ice. Finally, Sie et al. (2022) investigated the CO photodesorption yield dependence on ice thickness.

2017ApJ...835..169O___2016_Instance_1

Recent MIR studies have provided us with a much more realistic view of the central part of the AGNs. Spitzer studies of nearby Compton-thick AGNs have shown that even Compton-thick AGNs, especially low-luminosity ones, often show only modest to moderate silicate absorption at μm (e.g., Hao et al. 2007; Goulding et al. 2012). A classical smooth torus model, such as that of Pier & Krolik (1992), predicts deeper absorption in proportion to the X-ray absorption column density. On the other hand, if the torus is made of a collection of clouds, each cloud is heated to ∼300 K to emit MIR emission while absorbing the background light when the foreground cloud is cooler than the one behind. The radiation transfer effect among the clouds significantly reduces the net silicate absorption even when the torus is seen edge-on (Nenkova et al. 2002, 2008a, 2008b; Hönig et al. 2006; Hönig & Kishimoto 2010; Stalevski et al. 2012, 2016). Meanwhile, recent MIR interferometric studies of nearby AGNs have started to directly reveal the dust distribution in the vicinity of the AGNs at parsec scales. In some best-studied AGNs, extended optically thin dust emission elongated toward the system’s polar direction (e.g., direction of the extended narrow-line region or outflow) is typically found in addition to the compact disk-like component (e.g., Raban et al. 2009; Hönig et al. 2012, 2013; Tristram et al. 2012, 2014; López-Gonzaga et al. 2016; see also Asmus et al. 2016 for the single-dish study; see Netzer 2015 for a review). Such extended polar emission is clearly inconsistent with the classical idea of the dusty torus in the unification theory, and its nature is under debate. Some proposed ideas are that it originates from the inner funnel of an extended dust distribution above and below the torus and/or the dusty outflow within the ionizing cone that is radiatively driven from the inner wall of the compact dusty torus (e.g., Hönig et al. 2012, 2013; Keating et al. 2012; Roth et al. 2012; Tristram et al. 2014).

2021AandA...648A...3K__Lonsdale_et_al._2003_Instance_1

LoTSS is currently mapping all of the northern sky to a high sensitivity and resolution (S150MHz ~ 0.1 mJy beam−1 and FWHM ~ 6′′) at the relatively unexplored 120–168 MHz frequencies. In parallel with this, LOFAR is also undertaking deep observations of best studied multi-wavelength, degree scale fields in the northern sky, as part of the deep tier of LoTSS: the LoTSS Deep Fields (Tasse et al. 2021 and Sabater et al. 2021; hereafter Paper I and Paper II). The first three LoTSS Deep Fields are the European Large-Area ISO Survey-North 1 (ELAIS-N1; Oliver et al. 2000), Lockman Hole, and Boötes (Jannuzi & Dey 1999); these were chosen to have extensive multi-wavelength coverage from past and ongoing deep, wide-area surveys sampling the X-ray (e.g. Brandt et al. 2001; Hasinger et al. 2001; Manners et al. 2003; Murray et al. 2005), ultra-violet (UV; e.g. Martin et al. 2005; Morrissey et al. 2007) to optical (e.g. Jannuzi & Dey 1999; Cool 2007; Muzzin et al. 2009; Wilson et al. 2009; Chambers et al. 2016; Huber et al. 2017; Aihara et al. 2018) and to infrared (IR; e.g. Lonsdale et al. 2003; Lawrence et al. 2007; Ashby et al. 2009; Whitaker et al. 2011; Mauduit et al. 2012; Oliver et al. 2012) wavelengths; this is ideal for a wide range of our scientific objectives. These fields also benefit from additional radio observations at higher frequencies from the Giant Metrewave Radio Telescope (GMRT; e.g. Garn et al. 2008a,b; Sirothia et al. 2009; Intema et al. 2011; Ocran et al. 2019; Ishwara-Chandra et al. 2020) and the VLA (e.g. Ciliegi et al. 1999; Ibar et al. 2009). The current LoTSS Deep Fields dataset, covering ~ 26 deg2 (including multi-wavelength coverage) and reaching an unprecedented depth of S150MHz ~20 μJy beam−1, is comparable in depth to the deepest existing radio continuum surveys (e.g. VLA-COSMOS) but with more than an order of magnitude larger sky-area coverage. With this combination of deep, high-quality radio and multi-wavelength data over tens of square degrees, and along multiple sight-lines, the LoTSS Deep Fields are now able to probe a cosmological volume large enough to sample all galaxy environments to beyond z ~ 1, minimise the effects of cosmic variance (to an estimated level of ~4% for 0.5 z 1.0; Driver & Robotham 2010), and build statistical radio-selected samples of AGN and star-forming galaxies, even when simultaneously split by various physical parameters.

2020MNRAS.493.4950S__Haines_et_al._2015_Instance_1

In the framework of the hierarchical formation of structures, clusters of galaxies are continuously accreting galaxies. It has been suggested that in this process of falling, galaxies could undergo different physical processes that could affect the star formation even before they reach the cluster. Consequently, to fully understand what the cluster environment produces in galaxies, it is of key importance to have a throughout characterisation of the population of galaxies in the outskirts of clusters. Several observations have shown that properties of galaxies such as star formation, gas content, and colour are affected by the cluster environment at large clustercentric distances (e.g. Lewis et al. 2002; Solanes et al. 2002; Gómez et al. 2003; Braglia et al. 2009; Hansen et al. 2009; Park & Hwang 2009; von der Linden et al. 2010; Haines et al. 2015; Rhee et al. 2017). In particular, spiral galaxies with low star formation rates were found in the outskirts of clusters in early studies such as Couch et al. (1998) or Dressler et al. (1999). In recent years, a deficit of star-forming galaxies in the infalling region of clusters has been reported (e.g. Wetzel et al. 2013; Haines et al. 2015; Bianconi et al. 2018). This has been reproduced in simulations by Bahé et al. (2013). These results can be explained by the presence of environmental effects accelerating the consumption of the gas reservoir before galaxies enter in a cluster, a process known as pre-processing (e.g. Fujita 2004; Mihos 2004). An important fraction of the cluster galaxies has spent time in groups or filaments before they fall into the cluster (e.g. McGee et al. 2009; De Lucia et al. 2012; Wetzel et al. 2013; Hou, Parker & Harris 2014). The population of galaxies in the outskirts of clusters includes not only galaxies that have not yet entered the cluster but also backsplash galaxies, i.e. galaxies that have passed close to the centre of the cluster since their infall and are now beyond the virial radius (e.g. Mamon et al. 2004; Gill, Knebe & Gibson 2005; Mahajan, Mamon & Raychaudhury 2011). For an adequate characterisation of the properties of galaxies that are falling into clusters, it is important to take into account the contamination by backsplash galaxies, which, having orbited through the inner regions of a cluster, could have been affected by the physical processes present in that extreme environment. The backsplash scenario in the evolution of galaxies has also been explored in Rines & Diaferio (2005), Pimbblet et al. (2006), Aguerri & Sánchez-Janssen (2010), and Muriel & Coenda (2014).

2020AandA...641A.155V__Puglisi_et_al._2019_Instance_1

The scenario presented above has been formulated in various flavors to individually explain several of the properties reported here. The main addition of this work, namely the excitation of CO in distant main-sequence and starburst galaxies, fits in the general picture that we sketched. The ensemble of properties and correlations that we reported here can be also used to revisit the definition of what a starburst is. A standard operational classification is based on the distance from the observed empirical M⋆-SFR correlation, the main sequence. This proved to be a useful distinction and an excellent predictor of several trends (e.g., Sargent et al. 2014), but recent results, including our present and previous analysis (Puglisi et al. 2019), show that the demarcation between starburst and main-sequence galaxies is more blurred that we previously considered. We do detect starburst-like behaviors in galaxies on the main sequence (Elbaz et al. 2018), likely linked to the existence of transitional objects (Popping et al. 2017; Barro et al. 2017b; Gómez-Guijarro et al. 2019; Puglisi et al. 2019, and in prep. to limit the references to recent works based on submillimeter observations). Such transition might well imply an imminent increase of the SFR, driving the object in the realm of starbursts (e.g., Barro et al. 2017b), or its cessation, bringing the system back onto or even below the main sequence (Gómez-Guijarro et al. 2019; Puglisi et al. 2019), with the CO properties potentially able to distinguish between these two scenarios. Regardless of these transitional objects, a definition of starburst based on ΣSFR, rather than ΔMS, would naturally better account for the observed molecular gas excitation properties, dust temperatures and opacities, or SFE (see also Elbaz et al. 2011; Rujopakarn et al. 2011; Jiménez-Andrade et al. 2018; Tacconi et al. 2020). As an example, in Fig. 8 we show the mean SLED of the subsample of galaxies with both CO (2 − 1) and CO (5 − 4) coverage, split at its median ΣSFR. While only tentative at this stage, this suggests a trend of increasing CO excitation with ΣSFR, consistently with Fig. 7 and what mentioned above.

2017AandA...604A.118T__Zaritsky_et_al._1994_Instance_1

In the Local Universe, most of our current knowledge of chemical distributions in disc galaxies comes from studies of the ISM via observations of HII regions or young stellar populations. Current observations are consistent with the metallicity profiles of the ISM having negative metallicity gradients, on average. Metallicity gradients in units of dex kpc-1 determine tight correlations with the global properties of galaxies such as the stellar mass or the size, which are erased when normalised by a characteristics radius such as the half-mass radius (e.g. Zaritsky et al. 1994; Sánchez et al. 2013). There are few indirect estimations of the evolution of the stellar metallicity gradients using planetary nebulae (PN; e.g. Henry et al. 2010; Stanghellini et al. 2010). Maciel et al. (2003) calculated the gradients of SPs with different ages in the Milky Way disc, finding a signal of increasingly negative metallicity gradients for older stars. Recently Magrini et al. (2016) analysed the metallicity gradients determined by PN for four nearby galaxies, finding them to be flatter than those detected using HII regions. Surveys such as CALIFA provide detailed information on the properties of the ISM and the SPs, including their chemical abundances and age distributions on a variety of galaxies (Sánchez-Blázquez et al. 2014; González Delgado et al. 2015). The SDDS-IV MaNGA survey (Bundy et al. 2015) also investigate spatially resolved SPs and radial age and metallicity gradients for nearby galaxies (Li et al. 2015; Wilkinson et al. 2015) and will provide a large statistical sample to confront with models. The available observations of HII regions for high-z galaxies do not allow the formation of a robust conclusion on the evolution of the metallicity gradients (e.g. Yuan et al. 2011; Queyrel et al. 2012; Stott et al. 2014; Jones et al. 2015). In fact, high-redshift observations show a complex situation with gas-phase components showing a variety of metallicity gradients that could respond to the action of different physical processes (e.g. Cresci et al. 2010).

2020ApJ...897...73M__Coburn_2001_Instance_1

Some of the accretion-powered X-ray pulsars showed additional features in emission between the 10 and 20 keV energy bands and more rarely in absorption between 8 and 10 keV in their respective residuals when fitted with a variety of continuum models (Coburn et al. 2002). Coburn et al. (2002) argued that such features may be caused by inadequacies of the continuum model rather than cyclotron resonance features. For example, it was observed that a single emission line at around 14 keV can fit two features around 10 and 20 keV for Vela X-1 (Kreykenbohm et al. 2002), Her X-1 (Coburn 2001), and Cep X-4 (McBride et al. 2007), and an absorption line between 8 and 10 keV can fit the features for 4U 1907+09, 4U 1538–52, and 4U 0352+309 as in Figure 6 of Coburn et al. (2002). In the case of GRO J2058+42, an introduction of a single Gaussian emission line in this range of energies could not appropriately fit the spectrum. The results described in Section 3.3 using ratios of spectral counts with respect to the Crab spectrum derived for four different phases of the pulsar (Figure 6) clearly indicate the presence of prominent depressions around 10 keV and 20 keV for phase 1 in particular, and its presence in other phases as well. Therefore, it confirms the presence of such absorption features in the spectral data associated with a physical origin and not due to any discrepancies of the continuum model as discussed above. Additionally, it also excludes the possibility of any uncertainty in the response matrix of the detector as the response matrix was not used to deconvolve the spectrum to calculate these ratios. The relative significance of these absorption features that was subsequently estimated after modeling the data and results is shown in Table 2 for the phase-averaged case and in Table 3 for the four different pulse phases. These observations strongly favor the presence and detection of these absorption features in their respective pulsar spectra.

2017MNRAS.470.4099B__Smith_et_al._2003_Instance_1

Numerical simulations using the N-body method are the primary instrument used to probe the non-linear regime of structure formation in cosmology and provide the basis for all theoretical predictions for the distribution of dark matter at the corresponding physical scales. Over the last few decades, such simulations have gained in refinement and complexity and have allowed the exploration of an ever larger range of scales (for a review see e.g. Bertschinger 1998; Springel et al. 2005; Dehnen & Reed 2011). Nevertheless, the understanding of their precision and their convergence towards the continuum limit remains, at very least, incomplete, in particular for smaller scales (see e.g. Splinter et al. 1998; Knebe et al. 2000; Romeo et al. 2008; Joyce et al. 2009; Power et al. 2016). In this context ‘scale-free’ cosmological models, in which both the expansion law and the power spectrum characterizing the initial fluctuations are simple power laws, have the advantage of relative simplicity, and they have for this reason been studied quite extensively in the literature (see e.g. Efstathiou et al. 1988; Colombi et al. 1996; Bertschinger 1998; Jain & Bertschinger 1998; Smith et al. 2003; Knollmann et al. 2008; Widrow et al. 2009; Orban 2013; Diemer & Kravtsov 2015). More specifically these models provide a testing ground for the numerical method through the predicted ‘self-similarity’ of the clustering: the temporal evolution of the clustering statistics must be equivalent to a rescaling of the distances. This follows from the fact that there is only one characteristic length scale (derived from the amplitude of the fluctuations) and one characteristic time-scale in the model. Further the exact rescaling function can be determined from the evolution in the linear regime of arbitrarily small fluctuations. However, discreteness and numerical effects typically introduce additional characteristic scales (e.g. force regularization at small scales, particle density, finite box size, etc.) which lead directly to a breaking of such self-similarity. Thus the self-similarity of clustering provides a potentially powerful tool to separate the scales affected by such non-physical effects from the physical results representing the continuum limit. The focus of this study is to exploit self-similar models to better understand the resolution at small scales of N-body simulations. In particular, we will use simulations with a very small force smoothing which allow us to follow carefully the propagation of self-similarity to small scales in the course of a simulation.

2022ApJ...928...18Z__Eastwood_et_al._2017_Instance_1

The intensity of a geomagnetic storm is the result of the sustained interaction between the solar wind and the magnetosphere during the main phase of a storm. The most outstanding advantage of empirical formulae is their simplicity for space weather forecast: By inputting the solar wind parameters responsible for the main phase of a geomagnetic storm into an empirical formula, one can quickly get the estimated intensity of this geomagnetic storm. Of all empirical formulae, the Burton equation and OM equation have been used mostly to predict extreme geomagnetic storms. For example, the Burton equation was used to estimate the intensity of the storm that occurred on 1859 September 1–2 by Tsurutani et al. (2003), and Liu et al. (2014) used the OM equation to estimate the intensity of the storm on 2014 July 24. Extreme geomagnetic storms can cause widespread interference and damage to technological systems (Love 2021 and references therein) and then lead to significant economic loss (e.g., Council 2008; Schulte in den Bäumen et al. 2014; Eastwood et al. 2017; Riley & Love 2017). Hence, it is very important to ensure the accuracy of extreme geomagnetic storm forecasts. Now the question is whether the Burton equation or the OM equation can estimate the intensities of very large geomagnetic storms correctly. To answer this question, we compare the performance of the three models mentioned above using 15 great geomagnetic storms (ΔSYM-H ≤ −200 nT) that occurred during solar cycle 23. The solar wind parameters responsible for the main phases of the 15 storms are inputted into the three models to get the estimated intensities of the great geomagnetic storms (GGSs), which are compared with the observed intensities. To avoid any possible confusion, we use ΔSYM-H b , ΔSYM-Hom, and ΔSYM-H w to indicate the intensities of a geomagnetic storm estimated by the Burton equation, the OM equation, and the WCL equation, respectively, while we use ΔSYM-Hob to represent the observed intensity, namely the real variation of the SYM-H index during the main phase of a geomagnetic storm. The rest of this article is organized as follows. The data source and method are presented in Section 2, the results are presented in Section 3, and the discussion and summary are presented in Section 4.

2016MNRAS.461.4317M__Zharikov_et_al._2008_Instance_1

Finally, we compared the extinction-corrected upper limit on the optical flux of the PSR J0633+0632 PWN (Section 2.1) with its unabsorbed 0.3–10 keV X-ray flux. This is $F_{\rm X}^{{\rm pwn}} = 2.92^{+0.79}_{-0.81} \times 10^{-13}$ erg cm−2 s−1 (Abdo et al. 2013), computed by fitting the PWN area with an ellipse of semimajor and semiminor axis of 0.58 and 0.54 arcmin, respectively, oriented 130° due east (Marelli 2012). We subtracted the flux contribution of the point-like X-ray source south-west of the pulsar position (Fig. 2), which is only spatially coincident with the PWN. The extinction-corrected optical flux of the PWN in the g′ band is $F_{\rm opt}^{{\rm pwn}} \lesssim 9.8 \times 10^{-13}$ erg cm−2 s−1, integrated over the same area as used to compute the PWN X-ray flux. As done for the pulsars, we assumed the most conservative value of the interstellar extinction. This yields an optical-to-X-ray flux ratio of Fopt/Fx ≲ 4.6. PWNe have been detected both in the optical and X-rays around the Crab pulsar, PSR J0205+6449, PSR B0540−69, and PSR J1124−5916. Our upper limit on the Fopt/Fx for the PSR J0633+0632 PWN is above the values obtained for the other PWNe, which are typically ∼ 0.02–0.04, apart from the Crab PWN which has an Fopt/Fx ∼ 2 (Zharikov et al. 2008). This means that, owing to the faintness of the PSR J0633+0632 PWN in the X-rays, much deeper optical observations are needed to set similar constraints on its optical emission. We also compared the extinction-corrected optical spectral flux upper limit on the PWN in the g′ and r′-bands with the extrapolation of its X-ray spectrum in the optical domain. Like in Abdo et al. (2013), we used the best-fitting spectral index of the PWN, $\Gamma _{\rm X}^{{\rm pwn}} = 1.19^{+0.59}_{-0.22}$. The PWN SED is shown in Fig. 5. As seen, we cannot rule the presence of a spectral break between the optical and the X-ray energy range. A break in the optical/X-ray SED has been observed in other PWNe. For instance, the PWN around PSR B0540−69 features a clear break, with the optical fluxes being fainter than expected from the extrapolation of the X-ray PWN spectrum (Mignani et al. 2012). This is also the case for the PSR J1124−5916 PWN (Zharikov et al. 2008). A break in the opposite direction is observed in the SED of the PSR J1833−1034 PWN (Zajczyk et al. 2012), where the infrared fluxes (the PWN is not yet detected in the optical) are about two orders of magnitude above the extrapolation of the PWN X-ray spectrum. Only in the case of the Crab and PSR J0205+6449 PWNe, the PWN spectrum is compatible with a single PL, extending from the X-rays to the optical (Hester 2008; Shibanov et al. 2008). Optical detections of more PWNe through dedicated observing campaigns can allow one to relate the differences in the SEDs to the characteristics of the PWN.

2015MNRAS.446.4168R__Kroupa_2014_Instance_1

The IGIMF theory (Kroupa et al. 2013) predicts a coupling between some properties of a galaxy (the SFR and the metallicity) and the IMF. Since the IMF in turn strongly affects the dynamical evolution of the galaxy, the feedback between galaxy evolution and IMF is difficult and the fully complexity of a variable IMF has not been yet included in hydrodynamical simulations (but see Bekki 2013; Ploeckinger et al. 2014; Recchi 2014). Even the here treated approach based on the so-called simple model of chemical evolution leads to implicit integral equations that must be solved iteratively. We note in passing that the dependence of the IMF on the metallicity is well established theoretically, as low-metallicity gas cools less efficiently and self-gravitating clumps are more resilient to fragmentation. This leads to the formation of dense cores of higher mass. For this reason, the IMF is supposed to be extremely biased towards massive stars if the metallicity is smaller than ∼10−4 Z⊙ (Schneider et al. 2002). Also the dependence of the IMF on the SFR is nowadays observationally well established (Hoversten & Glazebrook 2008; Meurer et al. 2009; Gunawardhana et al. 2011; Kroupa 2014), and the IGIMF theory is the only existing computable access accounting for these observations (Weidner et al. 2013). Thus, in spite of the inherent complexity, detailed simulations of galaxy evolution based on variable IMFs need to be performed. With this paper, we aimed at showing in a simple setting how to take into account IMF variations in models of the chemical evolution of galaxies (see also Martinelli & Matteucci 2000; Calura et al. 2010). The method outlined here can be used in order to extend analytical studies of the evolution of galaxies based on simple models of chemical evolution (e.g. Spitoni et al. 2010; Lilly et al. 2013; Pipino et al. 2014). We described how the solution of the simple model differs from the standard, textbook analytical solutions of the simple model. We also showed (building on previous results of Köppen et al. 2007) that the IGIMF theory naturally leads to a MZ relation. In fact, low-mass galaxies are characterized on average by smaller SFRs. According to the IGIMF theory, a low SFR leads to a steep, top-light IMF, in which the production of heavy elements by massive stars is extremely limited. More massive galaxies instead produce many more massive stars because of the higher level of SFR, hence the attained present-day metallicity is larger.

2019MNRAS.484.1487E__Roca-Fabrega_et_al._2013_Instance_2

Since the manifold spirals arise in a system of reference which corotates with the bar, the manifold theory in its basic form predicts that the spiral arms should have the same pattern speed as the bar. This remark seems to come in conflict with observations both in our Galaxy (as reviewed e.g. in Bland-Hawthorn & Gerhard 2016; see also Antoja et al. 2014; Junqueira et al. 2015 and references therein) and in other galaxies (e.g. Vera-Villamizar et al. 2001; Boonyasait, Patsis & Gottesman 2005; Patsis, Kaufmann & Gottesman 2009; Meidt, Rand & Merrifield 2009; Speights & Westpfahl 2012; Speights & Rooke 2016). Considering again, galactic disc simulations, the leading paradigm over the years refers to simulations showing the coexistence of multiple pattern speeds (Sellwood & Sparke 1988; Little & Carlberg 1991; Rautiainen & Salo 1999; Quillen 2003; Minchev & Quillen 2006; Dubinski, Berentzen & Shlosman 2009; Quillen et al. 2011; Minchev et al. 2012; Baba, Saitoh & Wada 2013; Roca-Fabrega et al. 2013; Font et al. 2014; Baba 2015; but see also a noticeable exception in Roca-Fabrega et al. 2013), possibly connected also to the phenomenon of nonlinear coupling of multiple disc modes (Tagger et al. 1987; Tagger & Athanassoula 1991; Sellwood & Wilkinson 1993; Masset & Tagger 1997). On the other hand, it is well known that even isolated barred galaxies undergo substantial secular evolution (see Athanassoula 2013; Binney 2013; Kormendy 2013 in the tutorial volume Falcon-Barroso & Knapen 2013). The tendency to transfer angular momentum outwards (e.g. towards the halo or across the disc, Tremaine & Weinberg 1984; Debattista & Sellwood 1998; Debattista & Sellwood 2000; Athanassoula 2002; Athanassoula & Misiriotis 2002; Athanassoula 2003; OŃeill & Dubinski 2003; Holley-Bockelmann, Weinberg & Katz 2005; Berentzen, Shlosman & Jogee 2006; Martinez-Valpuesta, Shlosman & Heller 2006) leads the bar to slow down and grow in size at a rate which produces non-negligible change in dynamics at time-scales comparable even to a few bar periods. This process becomes complex, and even partially reversed due to the growth of ‘pseudo-bulges’ or peanuts (Kormendy & Kennicutt 2004), caused by dynamical instabilities such as chaos or the ‘buckling instability’ (Combes & Sanders 1981; Combes et al. 1990; Pfenniger & Friedli 1991; Raha et al. 1991; Bureau & Athanassoula 1999; Martinez-Valpuesta & Shlosman 2004; Bureau & Athanassoula 2005; Debattista et al. 2006). The reduction in size of the bar by the transfer of angular momentum under constant pattern speed is discussed in Weinberg & Katz (2007). Spiral activity acts as an additional factor of outwards transfer of angular momentum (Lynden-Bell & Kalnajs 1972), while a radial re-distribution of matter can take place even under a nearly preserved distribution of angular momentum (Hohl 1971; Sellwood & Binney 2002; Avila-Reese et al. 2005). Radial migration is enhanced by the amplification of chaos due to the overlapping of resonances among the various patterns (Quillen 2003; Minchev & Quillen 2006; Quillen et al. 2011).

2021ApJ...919..140S__Bartos_et_al._2017_Instance_2

Resonant dynamical friction may have applications beyond the relaxation of IMBHs examined in this paper. It may affect all objects in stellar clusters much more massive than the individual constituents of the disk, if present, including massive stars, stellar mass black holes (BHs), or the center of mass of massive binaries. Furthermore, it is also expected to operate in any type of disk with a high number of particles, including active galactic nucleus (AGN) accretion disks. Previously, it has been argued that stars and BHs crossing the disk on low-inclination orbits get captured by Chandrasekhar dynamical friction into the disk (Bartos et al. 2017; Panamarev et al. 2018; Tagawa et al. 2020). An interesting implication is that, if BHs settle into the disk, they interact dynamically and form BH–BH binaries efficiently, and frequent dynamical interactions and gas effects drive the BHs to merger, producing gravitational waves (GWs) detectable by LIGO, VIRGO, and KAGRA (McKernan et al. 2014, 2018; Bartos et al. 2017; Leigh et al. 2018; Yang et al. 2019; Tagawa et al. 2020, 2021; Samsing et al. 2020). Mergers are also facilitated by Lidov–Kozai oscillations in anisotropic systems (Heisler & Tremaine 1986; Petrovich & Antonini 2017; Hamilton & Rafikov 2019). The results in this paper show that resonant dynamical friction may accelerate the capture of objects in the accretion disks by a factor proportional to the SMBH mass over the local disk mass for large orbital inclinations. Pressure and viscosity in a gaseous disk do not inhibit the orbit-averaged torque from the IMBH, which leads to realignment and the warping of the disk (Bregman & Alexander 2012). Thus, RDF may efficiently catalyze the alignment of the orbital planes of BHs even in low-luminosity AGN or Seyfert galaxies with relatively small disk masses, which may not be possible for Chandrasekhar dynamical friction. In fact, this mechanism extends the scope of the “AGN merger channel” for GW source populations even beyond low-luminosity AGN and Seyfert galaxies, as it may organize BHs into disks also in nonactive galaxies with nuclear stellar disks.

2021AandA...645A.141V__Metcalfe_et_al._2016_Instance_1

This paper presents the results of our study of the effect of a dynamically adapting heat-conduction prescription, based on Kramers opacity law, in conjunction with semi-global MHD simulations. The main aim is to determine the effect of this prescription on the two major transitions reported in numerical studies (e.g., Gastine et al. 2014; Viviani et al. 2018). One concerns the rotation profiles, and is the transition from accelerated poles and decelerated equator to a solar-like profile, with a faster equator. The other involves the large-scale magnetic field, and is the transition from an axisymmetric magnetic field, as in the Sun, to a nonaxisymmetric magnetic field found in more rapid rotators. Previous studies (Viviani et al. 2018) found these transitions to occur at the same rotation rate, in contrast with the current interpretation of observations. The fact that simulations usually produce anti-solar differential rotation for the solar rotation rate could indicate that the Sun is in a transitional regime (e.g., Käpylä et al. 2014; Metcalfe et al. 2016), or could also mean that simulations still cannot fully capture the true rotational influence on turbulent convection in the Sun. Lehtinen et al. (2016) reported on the existence of nonaxisymmetric structures in stars with varying rotation rates, and were therefore able to determine quite a sharp transition point in terms of the rotation period, when fields turn from axi- to nonaxisymmetric configurations. According to dynamo theory, these two modes can compete, and there can be a transition region, where both dynamo modes co-exist, as is also clearly demonstrated by the models presented in this paper and those of Viviani et al. (2018). Therefore, the observational transition point must be regarded as a lower limit for the transition in terms of the rotation period, as it could be that the sensitivity of the current instruments is insufficient to detect the very weak nonaxisymmetric components. However, since active longitudes have not been detected on the Sun (Pelt et al. 2006), these two transitions should not be located at the same, nearly solar rotation rate.

2020ApJ...889...15Y__Yang_et_al._2016b_Instance_2

Although each one of the four aforementioned mechanisms has some observational support in certain systems, there is not a single mechanism that can explain all observed polarization in protoplanetary disks. Alignment with respect to the local radiation anisotropy (“k-RAT alignment” thereafter) is best supported by the azimuthal polarization pattern observed at ALMA Band 3 in the HL Tau system (Kataoka et al. 2017). However, it predicts a strong azimuthal variation of polarization and circular pattern (rather than elliptical pattern) (Yang et al. 2019). There is some tentative evidences for alignment with respect to the magnetic field, through either Radiative Alignment Torques (“B-RAT alignment”; Lazarian & Hoang 2007), or recently proposed Mechanical Alignment Torques (Hoang et al. 2018), in, e.g., the IRAS 4A system at cm wavelengths (Cox et al. 2015; Yang et al. 2016b) and BHB07-11 (Alves et al. 2018) at (sub)millimeter wavelengths. But there is no well-resolved system that matches the theoretical expectations (see, e.g., Cho & Lazarian 2007; Yang et al. 2016b; Bertrang et al. 2017) assuming the widely expected disk toroidal magnetic field yet (Flock et al. 2015). Mechanical alignment has recently received some attention. Hoang et al. (2018) claims that under MATs, grains can be aligned with respect to local dust-gas streaming direction, in the case of a weak or zero magnetic field, even if the velocity difference is sub-sonic. Within this picture, Kataoka et al. (2019) investigated the direction of streaming velocities for dust grains with different Stokes numbers, and the resulting polarization orientations. They found that their polarization pattern in the order-of-unity Stokes number case resembles that observed by Alves et al. (2018) in BHB07-11. The BHB07-11, however, is a binary system, and we expect more complicated velocity fields than the simple one assumed in Kataoka et al. (2019). Yang et al. (2019) investigated the observational features of another mechanical alignment mechanism, the Gold mechanism (Gold 1952), to address the circular versus elliptical pattern problem in the ALMA Band 3 polarization observations of HL Tau disk. However, they failed to explain the nonexistence of strong azimuthal variation, and suggested the scattering by dust grains aligned under the Gold mechanism may be the origin of the polarization at ALMA Band 3 in the HL Tau system.

2016MNRAS.455..449H__McGaugh_2012_Instance_1

With only six free parameters, the standard Λ cold dark matter (ΛCDM) cosmological model fits no less than 2500 multipoles in the cosmic microwave background (CMB) angular power spectrum (Planck Collaboration XVI 2014), the Hubble diagram of Type Ia supernovae, the large-scale structure matter power spectrum, and even the detailed scale of baryonic acoustic oscillations. It thus provides the current basis for simulations of structure formation, and is extremely successful down to the scale of galaxy clusters and groups. Nevertheless, it still faces numerous challenges on galaxy scales. Among these, the most important ones are the too-big-to-fail problem (Boylan-Kolchin, Bullock & Kaplinghat 2011) and the satellite-plane problem (e.g. Pawlowski, Pflamm-Altenburg & Kroupa 2012; Ibata et al. 2014) for dwarf galaxies, the tightness of the baryonic Tully–Fisher relation (McGaugh 2012; Vogelsberger et al. 2014), or the unexpected diversity of rotation curve shapes at a given mass scale (Oman et al. 2015). The latter problem is actually a subset of a more general problem, i.e. that the shapes of rotation curves indeed do not depend on the Dark Matter (DM) halo mass, contrary to what would be expected in ΛCDM, but rather on the baryonic surface density, as has long been noted (e.g. Zwaan et al. 1995). This makes the problem even worse, since the rotation curve shapes are not only diverse at a given mass scale, but uniform at a given baryonic surface density scale, implying a completely ununderstood fine-tuning of putative feedback mechanisms. On the other hand, this behaviour of rotation curves is an a priori prediction of the formula proposed by Milgrom more than 30 yr ago (Milgrom 1983a,b), relating the total gravitational field to the Newtonian field generated by baryons alone, and which can be interpreted as a modification of Newtonian dynamics on galaxy scales below a characteristic acceleration (Modified Newtonian Dynamics (MOND), for a review see Famaey & McGaugh 2012; Milgrom 2014). With this simple formula, high surface brightness (HSB) galaxies are predicted to have rotation curves that rise steeply before becoming essentially flat, or even falling somewhat to the not-yet-reached asymptotic circular velocity, while low surface brightness (LSB) galaxies are predicted to have rotation curves that rise slowly to the asymptotic velocity. This is precisely what is observed, and was predicted by Milgrom long before LSB galaxies were even known to exist. The formula also predicts the tightness of the baryonic Tully–Fisher relation.

2019MNRAS.488.5029H__Stacey_et_al._2010_Instance_3

For the first time, we detected [C ii] 158-μm emission from a GRB host galaxy at z > 2. This is the second detection of [C ii] 158-μm emission among known GRB host galaxies, following GRB 980425 (Michałowski et al. 2016). The [C ii] 158-μm fine structure line is the dominant cooling line of the cool interstellar medium, arising from photodissociation regions (PDR) on molecular cloud surfaces. It is one of the brightest emission lines from star-forming galaxies from FIR to metre wavelengths, almost unaffected by dust extinction. [C ii] 158-μm luminosity, L[C II], has been discussed as an indicator of SFR (e.g. Stacey et al. 2010). If L[C II] scales linearly with SFR, the ratio to FIR luminosity, L[C II]/LFIR, is expected to be constant, since LFIR is a linear function of SFR (e.g. Kennicutt 1998a). However, LC II/LFIR is not constant, but declines with increasing LFIR, known as the ‘[C ii] deficit’ (e.g. Luhman et al. 1998, 2003; Malhotra et al. 2001; Sargsyan et al. 2012; Díaz-Santos et al. 2013, 2017; Spilker et al. 2016). The [C ii] deficit persists when including high-z galaxies (e.g. Stacey et al. 2010; Wang et al. 2013; Rawle et al. 2014). In Fig. 5, we compare the [C ii] deficit in the GRB 080207 host and other star-forming galaxies. Two GRB hosts are shown by stars: GRB 080207 (orange star) and 980425 (blue star). The comparison sample is compiled from the literature up to z ∼ 3 (Malhotra et al. 2001; Cormier et al. 2010, 2014; Ivison et al. 2010; Stacey et al. 2010; Sargsyan et al. 2012; Farrah et al. 2013; Magdis et al. 2014; Brisbin et al. 2015; Gullberg et al. 2015; Schaerer et al. 2015). Active galactic nuclei are separated from star-forming galaxies based on either (i) the explicit description in the literature or (ii) EWPAH 6.2μm 0.1 (Sargsyan et al. 2012). As reported by previous studies (e.g. Maiolino et al. 2009; Stacey et al. 2010), high-z galaxies are located at a different place from local galaxies in the L[C II]/LFIR–LFIR plane.

2017MNRAS.472.1152R__Cenko_et_al._2010_Instance_1

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...889L..10M__McKay_et_al._2018_Instance_2

As stated earlier, during review of this manuscript Croviser et al. announced in a CBET a tentative water production rate approximately five times larger than our reported value. While the brief nature of the CBET precludes a detailed comparison, we discuss some possible reasons for this discrepancy. At the high airmass of these observations and the small dimensions of the ARCES slit, differential refraction can result in wavelength-dependent slit loss, which can skew flux measurements. However, this is not expected for [O i] 6300 Å emission because this feature is close to the guiding wavelength (∼5500 Å). We confirmed that this is indeed negligible for [O i] 6300 Å emission based on observations of comet C/2012 S1 (ISON) that were performed at a similarly high airmass with ARCES, and found that the production rates derived from the ISON [O i] 6300 Å measurements were consistent with values determined using other methods (McKay et al. 2018). Therefore, we do not consider this or other airmass-dependent phenomena as the reason for the discrepancy. At certain geocentric velocities the cometary [O i] 6300 Å emission sits on top of a strong telluric absorption, and at high airmass inaccurate removal of this feature can result in a decrease in the measured flux and therefore production rate. This was observed for C/2012 S1 (ISON) (McKay et al. 2018). However, the geocentric velocity of 2I/Borisov during our observations was ∼−35 km s−1, while the effect on observed [O i] 6300 Å line fluxes in comet ISON was only observed at geocentric velocities of ∼−50 km s−1. Therefore, this is also not a likely candidate to explain the discrepancy. It is also possible that the activity is highly variable, and we observed Borisov at a minimum in activity, while the Nançay observations, which were coadded over three weeks of observations, provide a long-term average production rate. However, no such variability is observed for CN, with the CN production rate being fairly constant over a several week period (Kareta et al. 2019; Opitom et al. 2019).

2020MNRAS.493.4950S__Linden_et_al._2010_Instance_1

In the framework of the hierarchical formation of structures, clusters of galaxies are continuously accreting galaxies. It has been suggested that in this process of falling, galaxies could undergo different physical processes that could affect the star formation even before they reach the cluster. Consequently, to fully understand what the cluster environment produces in galaxies, it is of key importance to have a throughout characterisation of the population of galaxies in the outskirts of clusters. Several observations have shown that properties of galaxies such as star formation, gas content, and colour are affected by the cluster environment at large clustercentric distances (e.g. Lewis et al. 2002; Solanes et al. 2002; Gómez et al. 2003; Braglia et al. 2009; Hansen et al. 2009; Park & Hwang 2009; von der Linden et al. 2010; Haines et al. 2015; Rhee et al. 2017). In particular, spiral galaxies with low star formation rates were found in the outskirts of clusters in early studies such as Couch et al. (1998) or Dressler et al. (1999). In recent years, a deficit of star-forming galaxies in the infalling region of clusters has been reported (e.g. Wetzel et al. 2013; Haines et al. 2015; Bianconi et al. 2018). This has been reproduced in simulations by Bahé et al. (2013). These results can be explained by the presence of environmental effects accelerating the consumption of the gas reservoir before galaxies enter in a cluster, a process known as pre-processing (e.g. Fujita 2004; Mihos 2004). An important fraction of the cluster galaxies has spent time in groups or filaments before they fall into the cluster (e.g. McGee et al. 2009; De Lucia et al. 2012; Wetzel et al. 2013; Hou, Parker & Harris 2014). The population of galaxies in the outskirts of clusters includes not only galaxies that have not yet entered the cluster but also backsplash galaxies, i.e. galaxies that have passed close to the centre of the cluster since their infall and are now beyond the virial radius (e.g. Mamon et al. 2004; Gill, Knebe & Gibson 2005; Mahajan, Mamon & Raychaudhury 2011). For an adequate characterisation of the properties of galaxies that are falling into clusters, it is important to take into account the contamination by backsplash galaxies, which, having orbited through the inner regions of a cluster, could have been affected by the physical processes present in that extreme environment. The backsplash scenario in the evolution of galaxies has also been explored in Rines & Diaferio (2005), Pimbblet et al. (2006), Aguerri & Sánchez-Janssen (2010), and Muriel & Coenda (2014).

2020MNRAS.496..152R__Springel_&_Hernquist_2002_Instance_1

From observations it is known that the interstellar gas has a complex structure with different phases – a hot volume-filling phase and a dense cold gas phase – both of which should be represented in galaxy formation simulations (see Naab & Ostriker 2017; Tumlinson et al. 2017). To model this, we treat the gas as a multiphase medium with many co-existing phases (see Marri & White 2003). We let two SPH particles i and j decouple into separate phases, if the following two conditions apply (Aumer et al. 2013): (1)$$\begin{eqnarray*} \max \left(\frac{A_i}{A_j}, \frac{A_j}{A_i} \right) \gt 50 , \quad -\mu _{ij} \lt c_{ij} . \end{eqnarray*}$$Here, Ai,j are the entropic functions of the particles (Springel & Hernquist 2002), $\mu _{ij} := (\boldsymbol{v}_i - \boldsymbol{v}_j) \cdot \frac{\boldsymbol{r}_i - \boldsymbol{r}_j}{|\boldsymbol{r}_i - \boldsymbol{r}_j|}$ is the relative velocity of the particles along their vector of separation, and cij is the pair-averaged sound speed. Two SPH particles decouple if their entropy (actually their entropic functions2) are very different unless they approach faster than with the local sound speed. The velocity restriction (equation 1) is required to capture shocks properly (Marri & White 2003). This multiphase treatment results in a continuum of phases from cold to hot and the results are not very sensitive to the exact ratio in equation (1). This model is aimed at preventing overcooling, i.e. artificially short cooling times (see e.g. Naab & Ostriker 2017, for a review) and allows for the simultaneous representation of, and energy injection into, a hot and a cold phase on the resolution scale (see Scannapieco et al. 2006, for a detailed discussion). Such multiphase ISM structure naturally arises in much higher resolution simulations of the supernova-driven multiphase ISM (see e.g. Walch et al. 2015). For further details on the multiphase model, see Marri & White (2003) and Aumer et al. (2013, 2014).

2021MNRAS.504.5702W__Werk_et_al._2014_Instance_1

Another notable accretion-regulated halo property is gas temperature, with the CGM of haloes nominally having both a hot and cold-phase. The hot coronal gas phase (at ≈Tvir) originates from the virial shock-heating of gas accreting high-mass haloes ($M_{\rm halo}\gtrsim 10^{12}\, \mathrm{M}_{\odot }$; e.g. Rees & Ostriker 1977). A cold-phase of the CGM at ≈104 K has also been observed, but its origins are less clear (e.g. Adelberger et al. (e.g. Adelberger et al. 2003; Stocke et al. 2006; Lehner & Howk 2011; Prochaska, Hennawi & Simcoe 2013; Werk et al. 2014; Zhu et al. 2014; Heckman et al. 2017; Zahedy et al. 2019). Several origins of this cool CGM phase have been proposed, namely pristine IGM accretion (e.g. simulation-based findings in van de Voort & Schaye 2012; Afruni, Fraternali & Pezzulli 2019, 2021), the condensation of hot halo gas (e.g. the empirical arguments of Voit 2018 and illustris-TNG findings in Nelson et al. 2020), feedback-driven outflows (e.g. Bouché et al. 2013; Borthakur et al. 2015; Anglés-Alcázar et al. 2017; Oppenheimer et al. 2018; Hafen et al. 2019), and the stripping of satellite galaxies in larger systems (e.g. Hafen et al. 2019 using the fire-2 simulations). Afruni et al. (2019, 2021), using semi-analytic models and results from the COS-Halos and COS-GASS surveys, argue that star-formation-driven outflows cannot account for the amount of cool gas in the CGM of observed haloes, pointing towards IGM accretion as the origin of this gas. Additionally, the radial variation of CGM properties was explored in Fielding et al. (2020) using a number of hydrodynamical simulations (as part of the smaug project). They find that the properties of the outer-CGM (at ≳0.5R200,crit) are shaped by larger scale processes, such as cosmological accretion, rather than galactic feedback that dominates the inner regions, ≲0.5R200,crit. In any case, the wide range of observed metallicities and temperatures observed implies that the CGM is a diverse, multi-phase gas reservoir, making it an ideal laboratory to study the influence of cosmological inflows.

2021MNRAS.508.2583Z__Schöier_et_al._2002_Instance_1

Located in the star-forming region ρ-Ophiuchi, inside the dark cloud L1689N and at a distance of 141 pc (Dzib et al. 2018), IRAS16293−2422 is a well-studied Young Stellar Object (YSO) classified as a Class 0 source with less than 104 yr (Andre, Ward-Thompson & Barsony 1993), and represents one of the very early stages of low-mass star formation. It was the first source identified as a hot corino (Blake et al. 1994; van Dishoeck et al. 1995) based on the detection of Complex Organic Molecules (COMs) in the source, which was later supported by follow-up studies (Ceccarelli et al. 1998, 2000; Schöier et al. 2002; Crimier et al. 2010; Jørgensen et al. 2011, 2016; Pineda et al. 2012; Oya et al. 2016; Jacobsen et al. 2018; van der Wiel et al. 2019). Higher resolution observations revealed that IRAS16293−2422 is in fact a triple system, composed of sources A1 and A2, separated by 54 au from each other (Maureira et al. 2020) and source B, 738 au (5 arcse; Wootten 1989) away from source A. Due to this larger separation, tidal truncation between the three protostars is discarded and therefore source B is considered to have evolved as an isolated source (Rodríguez et al. 2005). It was initially proposed to be either an evolved T Tauri star (Stark et al. 2004; Takakuwa et al. 2007) or a very young object (Chandler et al. 2005), however, Chandler et al. (2005) suggested that source B has large-scale infalls based on SO line emission. Pineda et al. (2012) confirmed the infall of an inner envelope, with mass accretion rates of 4.5 × 10−5 M⊙yr−1, based on ALMA detections of inverse P-Cygni profiles in CH3OCHO-E, CH3OCHO-E-A and H2CCO, ruling out the possibility of it being a T Tauri star. The interpretations of infall from these profiles was also suggested by Jørgensen et al. (2012) and Zapata et al. (2013). Unlike the A1 and A2 protostars, source B has not shown clear signs of outflow launching, explained by the lack of free–free emission at low frequencies (Chandler et al. 2005; Rodríguez et al. 2005; Loinard et al. 2007; Rao et al. 2009; Liu et al. 2018; Hernández-Gómez et al. 2019b) and also based on molecular lines (Loinard et al. 2002; van der Wiel et al. 2019).

2020ApJ...898...52M__Elmegreen_&_Scalo_2004_Instance_1

Idealized simulations have the advantage of carefully controlled conditions but the disadvantages that the turbulence is driven in an artificially prescribed manner to maintain a fixed overall turbulent amplitude and the processes leading to cloud formation and destruction are not followed. In reality, GMCs form due to a combination of large-scale ISM flows (including turbulence, shear, and epicyclic motion) and gravity (both stellar gravity and self-gravity) that lead to collection of material from a large volume, as mediated by thermal and magnetic pressure, and a change from the atomic to the molecular phase as the gas cools (e.g., McKee & Ostriker 2007; Dobbs et al. 2014; Chevance et al. 2020). Turbulence on scales less than the scale height of the warm–cold ISM likely originates primarily due to the feedback from young stars (Elmegreen & Scalo 2004; Mac Low & Klessen 2004; McKee & Ostriker 2007),3 3 Gravitational instabilities in the combined gas–stellar system (e.g., Jog & Solomon 1984; Romeo 1992; Rafikov 2001; Kim & Ostriker 2007) can drive horizontal motions at very large scales, as seen in numerical simulations (e.g., Kim & Ostriker 2007; Shetty & Ostriker 2008; Agertz et al. 2009; Dobbs et al. 2011; Hopkins et al. 2012; Agertz & Kravtsov 2015, and citations within), but these motions generally do not reach supersonic amplitudes unless they are associated with gravitational collapse. In addition, turbulence at scales less than the disk scale height can be driven by spiral shocks and the magnetorotational instability, but numerical simulations show that the corresponding amplitudes are relatively modest in cold gas (e.g., Wada & Koda 2004; Piontek & Ostriker 2005, 2007; Kim et al. 2006, 2010; Dobbs & Bonnell 2007; Bonnell et al. 2013, and citations within). whether inherited from a GMC’s formation stage or produced internally. Considering that GMCs live for at most a few turbulent crossing or freefall times (Kawamura et al. 2009; Kruijssen et al. 2019), it is not clear that internal GMC conditions can control star formation in a way that is entirely divorced from their formation and destruction processes.

2020MNRAS.497..302T__Watarai_2006_Instance_1

In this paper, we explore the conditions required for hyper-Eddington accretion on to a BH when both radiative and mechanical feedback operate simultaneously, performing two-dimensional (2D) hydrodynamical simulations with multifrequency radiation transfer. We conduct a comprehensive survey on the parameter dependence of outflow models, varying the outflow opening angle, mass loading degree into outflows, velocity of outflows, and density of gas surrounding the BH. To model mechanical feedback, we adopt a phenomenological model proposed by Ostriker et al. (2010), while radiative feedback is treated by adopting the standard and slim disc model (Shakura & Sunyaev 1973; Abramowicz et al. 1988; Watarai 2006) as in Takeo et al. (2019). We find that the flow structure consists of two distinct parts in the early sub-Eddington phase; the bipolar outflowing region heated up to T ∼ 106−7 K due to strong shock and the equatorial inflowing region where ionized gas is mildly heated to T ∼ 105 K due to photoionization. When the ambient gas density exceeds a critical threshold, as in the cases where only radiative feedback is included (Inayoshi et al. 2016; Takeo et al. 2018), the mass accretion rate on to the nuclear region rises to a hyper-Eddington value. Since mechanical power of outflows driven by the rapidly accreting BH is sufficiently strong, bipolar outflows completely evacuate the surrounding gas in the polar region and reduce the mass inflow (BH accretion) rate by a factor of ≈3–13 (≈6–26, respectively) from the case without mechanical feedback. Furthermore, we find that the critical gas density required for hyper-Eddington accretion is reduced by a factor of ∼3 and the transition occurs in a shorter dynamical time-scale when mechanical feedback is modelled in the simulations. In fact, the effects that alleviate the transition to rapid accretion tend to be more prominent as the outflow is stronger, i.e. a wider opening angle, higher mass loading factor, and higher outflow velocity. This is because suppression of BH accretion owing to outflows reduces the radiative output from the nuclear BH, leading to hyper-Eddington accretion.

2019AandA...630A..30L__Hässig_et_al._(2015)_Instance_3

The many unexpected surprises of comet 67P/Churyumov-Gerasimenko (hereafter 67P) revealed by the historic Rosetta mission highlight the importance of observing the evolution of comets throughout their orbits. One of the surprises was the drastic heterogeneity in both the major and minor volatile species in the coma that was observed early on in the mission (Hässig et al. 2015; Luspay-Kuti et al. 2015, hereafter ALK15). When Rosetta first arrived at comet 67P in August 2014, the Rosetta Orbiter Mass Spectrometer for Ion and Neutral Analysis/Double Focusing Mass Spectrometer (ROSINA/DFMS; Balsiger et al. 2007) detected large diurnal variations in the intensity profiles of various species in the coma from distances to the comet as far as 250 km. At this time, 67P was still at a distance of about 3 AU and inbound from the Sun. The intensity variations in the major and minor volatile species were found to be periodic, and were dependent on both the observing sub-spacecraft latitude and longitude (Hässig et al. 2015; Luspay-Kuti et al. (2015)). As reported in Hässig et al. (2015), the intensity of H2O in the coma dominated the overall signal, with maxima in the H2O signal every ~6 h, about twice during a rotation. Interestingly, however, CO2 and CO displayed a separate additional maximum when the H2O signal was near its minimum. This independent maximum in CO2 and CO only occurred at negative observing latitudes that are associated with a particular “view” of Rosetta at 67P, with the larger lobe blocking out the neck and head. At this time, 67P had not yet reached its first equinox (10 May 2015), and the poorly illuminated southern hemisphere was experiencing winter. In addition, the largest H2O activity was localized at the well-illuminated neck region, as also seen by the Microwave Instrument on the Rostta Orbiter (MIRO; Gulkis et al. 2015; Biver et al. 2015; Lee et al. 2015) and by the Visible InfraRed Thermal Imaging Spectrometer (VIRTIS; Bockelée-Morvan et al. 2015; Migliorini et al. 2016). VIRTIS also measured weak H2O production in regions with low solar illumination, while CO2 was outgassing from both illuminated and non-illuminated regions pre-inbound equinox (Bockelée-Morvan et al. 2015; Migliorini et al. 2016; Fink et al. 2016). The observed outgassing pattern of the major cometary species suggested that CO and CO2 may be sublimating from a depth below the diurnal skin depth.

2021AandA...645A..99C__analysis,_Uttley_et_al._(2011)_Instance_2

X-ray reverberation in black hole X-ray binaries was first robustly detected in GX 339–4 by Uttley et al. (2011) when the source was in its hard state. Previous studies of GX 339–4 pointed to the approximate central mass being ≥6 M⊙ (e.g. Hynes et al. 2003) and a small disc inclination angle (De Marco et al. 2015). Miller et al. (2008) fitted the Suzaku spectra and found that the central black hole has a very high spin, a ∼ 0.998. The X-ray spectroscopic analysis of the hard state spectra from the RXTE archive carried out by García et al. (2015) suggested the black hole spin to be a ∼ 0.95. Spectral fitting of GX 339–4 during its very high flux state using NuSTAR and Swift also suggested a high spin of a ∼ 0.95 (Parker et al. 2016). According to the time-lag analysis, Uttley et al. (2011) found that the disc thermal emission (∼0.3–0.7 keV, soft band) leads the power-law variations (∼0.7–1.5 keV, hard band) on long timescales (> 1s). Mahmoud et al. (2019) assumed that the soft component that leads the power-law emission is a soft Comptontized component. Rapisarda et al. (2016) and Rapisarda et al. (2017) instead modelled it as a variable inner region of the thin disc. However, the disc black-body variations lag behind the power-law variations by a few milliseconds on short timescales ( 1s). This switch from low-frequency hard to high-frequency soft lags is thought to be produced by two distinct mechanisms. While the hard lags are likely due to inward propagating fluctuations (e.g. Kotov et al. 2001; Arévalo & Uttley 2006), the soft lags can be explained by thermal reverberation associated with the longer light-travel time the hard photons take from the central power-law X-ray source to the disc where they are reprocessed into relatively soft black-body emission. The thermal reverberation lags then provide clues to the geometry of the X-ray source and the inner accretion flow close to the event horizon of the central black hole.

2022ApJ...929...65I__Ikhsanov_2002_Instance_1

Our parameter range reaches into the “propeller” regime, where the azimuthal velocity of the star’s outer magnetosphere exceeds the Keplerian velocity at disk truncation (Romanova et al. 2005), i.e., where the disk is truncated near and outside of the corotation radius. Observations of the propeller regime have been discussed in relation to various astrophysical systems, such as rapidly rotating neutron stars, white dwarfs in cataclysmic variables, and CTTS (see, e.g., Stella et al. 1986; Treves et al. 1993; Cui 1997; Alpar 2001; Ekşı & Alpar 2003; Mori & Ruderman 2003). The propeller regime has been investigated both analytically (Davies et al. 1979; Li & Wickramasinghe 1997; Lovelace et al. 1999; Ikhsanov 2002; Rappaport et al. 2004; Ekşi et al. 2005), and via MHD simulations (Wang & Robertson 1985; Romanova et al. 2003, 2004, 2005, 2009; Ustyugova et al. 2006). It has been shown that, for a rapidly rotating star with a strong magnetic field, a significant proportion of accreting material is in fact centrifugally expelled as it reaches the inner region of the disk and is redirected as a propeller-driven outflow, allowing the star to rapidly spin down (Davidson & Ostriker 1973; Illarionov & Sunyaev 1975; Lipunov 1992). For example, MHD simulations performed by Romanova et al. (2005) and Zanni & Ferreira (2013) demonstrate the following quasi-periodic behavior: (1) disk material accumulates at the inner region of the disk, which moves the truncation radius closer to the star; (2) some material can then accrete onto the star, reducing the mass of material at the disk inner edge; (3) remaining inner-disk material gains angular momentum and is ejected at the centrifugal barrier, which moves the truncation radius further away from the star, for the cycle to repeat. In this regime, accretion becomes intermittent, or can even be inhibited completely, and the net effect acts to remove angular momentum from the star. Therefore, it is possible that the propeller mechanism is responsible for the slow rotation rates observed in CTTS.

2016ApJ...833....7Y__Owen_&_Wu_2013_Instance_3

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.

2021MNRAS.504.5992M__Ho_et_al._2015_Instance_1

Characterizing and understanding the distribution and transport of chemical elements inside galaxies is a critical aspect of galaxy evolution. Successive generations of star formation enrich the interstellar medium (ISM) with metals. Therefore, the spatial distribution of chemical abundances in galaxies is a powerful tracer of the history of gas flows, star formation, accretion, and mergers throughout their assembly (e.g. Edmunds & Greenhow 1995; Kewley et al. 2010; Torrey et al. 2012; Finlator 2017; Ma et al. 2017; Bresolin 2019; Hemler et al. 2020). Over the last few decades, our understanding of chemical inhomogeneities in galaxies has advanced dramatically, largely due to the advent of integral field unit (IFU) spectroscopy (see Maiolino & Mannucci 2019 for a review). Negative radial metallicity gradients have been widely observed in the low-redshift galaxy population (e.g. Searle 1971; Vila-Costas & Edmunds 1992; Berg et al. 2013, 2020; Ho et al. 2015; Belfiore et al. 2017; Poetrodjojo et al. 2018) with other studies additionally observing azimuthal variations from these radial metallicity trends (Li, Bresolin & Kennicutt 2013; Vogt et al. 2017; Ho et al. 2018, 2019; Kreckel et al. 2020). Observational limitations mean that metallicities are more challenging to measure at high redshift. In the absence of gravitational lensing, spatial resolution is reduced. Furthermore, fainter targets mean that metallicities are typically derived from fewer emission lines, limiting our ability to control for possible redshift evolution in the ISM conditions of galaxies when constructing an abundance scale (see Appendix A for a more comprehensive discussion). Existing determinations of radial gradients in high-redshift galaxies are generally limited to modest samples of lensed galaxies, or samples of the largest disc galaxies, and show substantial amounts of scatter from steep negative gradients to positive gradients (Yuan et al. 2011; Swinbank et al. 2012; Jones et al. 2013; Leethochawalit et al. 2016; Wuyts et al. 2016; Carton et al. 2018; Wang et al. 2019a,b; Curti et al. 2020b; Gillman et al. 2021). Extending observations to smaller, fainter galaxies remains a challenge.

2017MNRAS.464..635M__Dekel_et_al._2009_Instance_3

The basic idea, summarized in Dekel et al. (2009), is that during VDI, the high surface density of gas and ‘cold’ young stars, Σ, drives the Toomre Q parameter below unity, Q ∼ σΩ/(πGΣ) ≲ 1, where σ is the 1D velocity dispersion and Ω is the angular frequency, a proxy to the epicyclic frequency κ, which is related to the potential well (Toomre 1964). It has been established that under such conditions, the disc will fragment and produce large star-forming clumps. This has been shown using idealized simulations of isolated galaxies (Noguchi 1999; Gammie 2001; Immeli et al. 2004a,b; Bournaud, Elmegreen & Elmegreen 2007; Elmegreen, Bournaud & Elmegreen 2008; Bournaud & Elmegreen 2009; Hopkins et al. 2012b), as well as cosmological simulations (Agertz, Teyssier & Moore 2009; Ceverino et al. 2010; Ceverino et al. 2012; Genel et al. 2012; Mandelker et al. 2014; Oklopcic et al. 2016). The ratio of clump mass to the mass of the cold disc scales as Mc/Md ∝ δ2, where δ = Md/Mtot is the ratio of the cold disc mass to the total mass within the disc radius, which includes the bulge and dark matter halo (e.g. Dekel et al. 2009). This leads to much larger clumps at z ∼ 2 than the low-redshift giant molecular clouds (GMCs). Gravitational interactions in the perturbed disc drive turbulence causing the disc to self-regulate in a marginally stable state with Q ≲ 1 (Dekel et al. 2009; Ceverino et al. 2010; Krumholz & Burkert 2010; Cacciato, Dekel & Genel 2012; Forbes, Krumholz & Burkert 2012; Forbes et al. 2014) that can last for more than a Gyr so long as the accretion is not interrupted. Some recent works have called into question the validity of linear Toomre analysis in the context of these highly non-linear galaxies (Behrendt, Burkert & Schartmann 2015; Tamburello et al. 2015; Inoue et al. 2016) and others have suggested alternate fragmentation mechanisms related to turbulence (e.g. Hopkins 2013). However, since clump formation is largely determined by the balance between self-gravity, turbulent pressure and the centrifugal force, the largest clumps are always roughly at the Toomre scale. Larger clumps would be disrupted due to the shear and/or tidal forces within the disc, or would not collapse in the first place due to the centrifugal force. Therefore, regardless of the full validity of linear Toomre analysis, it is plausible that the Toomre Q parameter can serve as a crude criterion for instability, possibly with a critical value that is larger than unity.

2019MNRAS.490.5722W__Remus_et_al._2013_Instance_2

Since it is infeasible to observe the evolution of individual galaxies over time, theoretical approaches focusing on understanding the formation of ETGs have made use of numerical simulations to trace the evolution of individual galaxies. Through zoom-in and cosmological simulations, a consensus has emerged between these simulations that the formation of ETGs proceeds through two phases, where galaxies first go through dissipative gas-rich wet mergers followed by in situ star formation bursts at redshifts above z ≈ 2, and then evolve towards low redshift through non-dissipative gas-poor dry mergers (Naab et al. 2007; Guo & White 2008; Hopkins et al. 2009; Nipoti et al. 2009b; Nipoti, Treu & Bolton 2009a; Oser et al. 2010; Johansson, Naab & Ostriker 2012; Moster, Naab & White 2013; Remus et al. 2013; Furlong et al. 2015; Wellons et al. 2015, 2016; Rodriguez-Gomez et al. 2016). However, regarding the redshift evolution of ETGs’ total power-law density slopes, no consensus has been reached neither among different cosmological hydrodynamic simulations nor between simulations and observations, despite the many advances in cosmological simulations (Vogelsberger et al. 2019a). While the Magneticum pathfinder simulation (Remus et al. 2017) and the Illustris simulations (Xu et al. 2017) produce shallower total density profile with time, the Horizon-AGN simulations (Peirani et al. 2019) produce steeper total density profile with time, in better agreement with the redshift evolution trend found in observations. However, the latter simulation has smaller slope values compared to the former two, which are closer to the observed slope values due to different implementation of feedback models, etc. Apart from cosmological simulations, dedicated zoom-in simulations (Johansson, Naab & Burkert 2009; Johansson et al. 2012; Remus et al. 2013) have revealed that dry mergers that dominate the passive evolution of ETGs below z ≈ 2 could make the total density profile shallower than isothermal (Hilz et al. 2012; Hilz, Naab & Ostriker 2013; Remus et al. 2017). The inclusion of wet mergers is also crucial for reconciling the simulated redshift evolution trend of the slope with strong-lensing observations (Sonnenfeld, Nipoti & Treu 2014).

2021MNRAS.503.2776Y__Ajith_et_al._2007_Instance_1

In order to investigate the signal-to-noise ratio (SNR), ρ of NS–WD binaries for LISA-type space GW detectors, we calculate the averaged square SNR $\overline{\rho ^{2}}$ over the sky location, inclination, and polarization as (30)$$\begin{eqnarray*} \overline{\rho ^{2}} = \int _{f_{1}}^{f_{2}}\frac{4\cdot \frac{4}{5}fA^{2}(f)}{(P_{\rm n}(f)/R(f))} \rm d (\ln \it f), \end{eqnarray*}$$(Moore, Cole & Berry 2015; Robson, Cornish & Liu 2019), where f1 and f2 are the lower and upper limits of the integral, respectively. The factor 4 in the numerator of the integrand comes from the addition of strain noise in the detector arms and the two-way noise in each arm (Larson, Hiscock & Hellings 2000). We calculate the GW amplitudes A(f) of NS–WD binaries using the phenomenological (PhenomA) waveform model in the Fourier domain (Ajith et al. 2007; Robson et al. 2019). A(f) is expressed as (31)$$\begin{eqnarray*} A(f) = \sqrt{\frac{5}{24}}\frac{G^{5/6}\mathcal {M}^{5/6}}{\pi ^{2/3}c^{3/2}R_{\rm b}}f^{-7/6}\, {\rm Hz}^{-1},\,\,\,\it f\lt f_{\rm m}, \end{eqnarray*}$$ (32)$$\begin{eqnarray*} f_{\rm m} = \frac{0.2974\zeta ^{2}+0.04481\zeta +0.09556}{\pi (GM/c^{3})}\, {\rm Hz}, \end{eqnarray*}$$ (33)$$\begin{eqnarray*} \zeta = m_{1}m_{2}/M^{2}, \end{eqnarray*}$$where $\mathcal {M} \equiv m_1^{3/5} m_2^{3/5}/(m_1+m_2)^{1/5}$, and fm is the GW frequency at the point of merging. If f > fm, the index of the power-law relation between A(f) and f changes (Ajith et al. 2007) and is beyond the scope of this study. The power spectral density of total detector noise $P_{\rm n}=\frac{1}{L^{2}}\left[P_{\rm o}+2(1+\cos ^{2}(f/f_{\ast }))\frac{P_{\rm a}}{(2\pi f)^{4}}\right]$, where f* = c/(2πL), L = 2.5 × 109 m is the armlength of the detector, $P_{\rm o}=2.25\times 10^{-22} \,\rm m^{2}\left(1+(\frac{2\,mHz}{\it f})^{4}\right) \,\, \rm Hz^{-1}$ is the single-link optical metrology noise, and $P_{\rm a}=9.0\times 10^{-30} \,\rm (m\,s^{-2})^{2}\left(1+(\frac{0.4\,mHz}{\it f})^{2}\right)\left(1+(\frac{\it f}{8\,\rm mHz})^{4}\right) \,\,Hz^{-1}$ is the single test mass acceleration noise (LISA Science Study Team 2018; Robson et al. 2019). R(f) is the transfer function numerically calculated from Larson et al. (2000). The effective noise power spectral density can be defined as Sn(f) = Pn(f)/R(f). For Taiji and Tianqin, we use the sensitivity curve data in Ruan et al. (2020) and Wang et al. (2019), respectively.

2022ApJ...924...42N__Torres_et_al._2013_Instance_1

In this model, the pulsar associated with the PWN loses its rotational energy via a pulsar wind composed of magnetic and high-energy particles to power the high-energy physical process inside the nebula (Atoyan & Aharonian 1996; Fang & Zhang 2010). The relativistic wind of particles driven by the pulsar is blown into the ambient medium and generates a termination shock wave, which accelerates the electrons to relativistic energy. These relativistic electrons interact with the magnetic field and low-energy background photons (the synchrotron, thermal, FIR, and microwave background radiation), and generate the multiband nonthermal photons with energies ranging from radio to high-energy gamma-ray bands (Zhang et al. 2008; Fang & Zhang 2010; Lu et al. 2017). According to the review of the leptonic model (see, e.g., Zhang et al. 2008; Venter & de Jager 2007; Torres et al. 2013), the electrons injected into PWNe are accelerated by the pulsar magnetosphere and the termination shock. Therefore, the relativistic particles injected into the PWNe are also assumed as two different power-law components from the pulsar magnetosphere and shock acceleration, respectively. The injected spectrum of relativistic electrons inside PWNe is described as 1 Q(Ee,t)=Q0(t)Ee/Ecut−α1ifEeEcutQ0(t)Ee/Ecut−α2ifEe≥Ecut, where the Q 0 can be determined by the ∫Q(E e , t)E e dE e =η L(t); η is the conversion efficiency from spin-down power into electron luminosity. The maximum energy of the electrons was express as Emax(t)≈ε0L(t)L0 , and L 0 is initial spin-down power. The electron energy distribution was given by 2 dN(Ee,Tage)dt=∫0TageQ(Ee,t)exp−Tage−tτeffdt, where the τeff−1=τesc(t)−1+τsyn(t)−1 , τ esc (t) is the escape timescale, and τ syn(t) is the lifetime of the relativistic electron of the synchrotron emission loss. The details of temporal evolution about the electron in the PWNe are discussed by the Zhang et al. (2008; also see the version of Fang & Zhang 2010; Lu et al. 2017).

2015AandA...584A.103S__Chamel_et_al._2011_Instance_3

Douchin & Haensel (2001; DH) formulated a unified EoS for NS on the basis of the SLy4 Skyrme nuclear effective force (Chabanat et al. 1998), where some parameters of the Skyrme interaction were adjusted to reproduce the Wiringa et al. calculation of neutron matter (Wiringa et al. 1988) above saturation density. Hence, the DH EoS contains certain microscopic input. In the DH model the inner crust was treated in the CLDM approach. More recently, unified EoSs for NS have been derived by the Brussels-Montreal group (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013). They are based on the BSk family of Skyrme nuclear effective forces (Goriely et al. 2010). Each force is fitted to the known masses of nuclei and adjusted among other constraints to reproduce a different microscopic EoS of neutron matter with different stiffness at high density. The inner crust is treated in the extended Thomas-Fermi approach with trial nucleon density profiles including perturbatively shell corrections for protons via the Strutinsky integral method. Analytical fits of these neutron-star EoSs have been constructed in order to facilitate their inclusion in astrophysical simulations (Potekhin et al. 2013). Quantal Hartree calculations for the NS crust have been systematically performed by (Shen et al. 2011b,a). This approach uses a virial expansion at low density and a RMF effective interaction at intermediate and high densities, and the EoS of the whole NS has been tabulated for different RMF parameter sets. Also recently, a complete EoS for supernova matter has been developed within the statistical model (Hempel & Schaffner-Bielich 2010). We shall adopt here the EoS of the BSk21 model (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013; Goriely et al. 2010) as a representative example of contemporary EoS for the complete NS structure, and a comparison with the other EoSs of the BSk family (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013) and the RMF family (Shen et al. 2011b,a) shall be left for future study.

2021ApJ...922L..11H__Castaing_et_al._1990_Instance_1

Most of these observations are consistent with the general understanding that, for increasing distance from the Sun, the turbulent power-law spectrum expands toward larger scales (Bavassano et al. 1982; Bruno & Carbone 2013; Chen et al. 2020). However, spectral properties may be misleading, as it is not possible to unequivocally ascribe the Kolmogorov-like power-law scaling to the presence of a turbulent cascade. For example, it is universally observed that turbulence is associated with intermittency (Kolmogorov 1962), as is routinely observed in solar wind measurements (Sorriso-Valvo et al. 1999; Bruno & Carbone 2013). A standard way for characterizing the intermittency of a field ϕ (ϕ being, for example, a velocity or magnetic field component) is by means of the scale-dependent increments Δϕ = ϕ(t + Δt) − ϕ(t), which account for the presence of gradients on a timescale Δt (Anselmet et al. 1984). Intermittency is related to the scale-dependent shape of the probability distribution function of the increments Δϕ (Castaing et al. 1990). This, additionally, implies the existence of nonvanishing odd moments. In particular, a scaling law can be derived for the third-order moment directly from the dynamical MHD equations, as the conservation law of the appropriate inviscid invariants (de Karman & Howarth 1938; Danaila et al. 2001). Such a relation, known in the MHD description as the Politano–Pouquet (PP) law (Politano & Pouquet 1998; Carbone et al. 2009), establishes that under the hypothesis of homogeneity, stationarity, local isotropy, and incompressibility, in the turbulent inertial range the mixed third-order moment of the increments of velocity (v) and magnetic field (in velocity units, b = B / 4 π ρ , with B the magnetic field vector and ρ the plasma mass density) is a linear function of the scale Δt. Moreover, the proportionality coefficient is related to the mean energy transfer rate of the turbulent cascade. This can be written as 1 Y ( Δ t ) ≡ 〈 Δ v R ( ∣ Δ v ∣ 2 + ∣ Δ b ∣ 2 ) − 2 Δ b R ( Δ v · Δ b ) 〉 = 4 3 ε V sw Δ t . Here Δv and Δb are scale-dependent vector increments of the plasma velocity and magnetic field, as defined above; ΔvR = vR(t + Δt) − vR(t) and ΔbR = bR(t + Δt) − bR(t) are velocity and magnetic field longitudinal increments measured in the sampling direction R; ε is the mean energy transfer rate; and brackets indicate ensemble average. Note that the solar wind speed Vsw is used for switching between space scales, ℓ, and timescales, Δt, through the Taylor hypothesis, ℓ = VswΔt (Taylor 1938). This also results in the reversal of the sign in the left-hand side of Equation (1) with respect to the traditional formulation in terms of spatial increments. The PP law is a fundamental relation for MHD turbulence, since it describes the energy cascade and ultimately allows us to estimate the energy that will be dissipated at small scales. It is particularly relevant for solar wind turbulence, where the collisionless processes responsible for removing the energy at the bottom of the nonlinear cascade are not yet fully understood (Chen et al. 2019; Sorriso-Valvo et al. 2019; Matthaeus et al. 2020; Smith & Vasquez 2021). The linear relation (1) has been observed in various regions of the heliosphere, for different conditions of the space plasma, confirming the turbulent nature of their dynamics and providing an estimate of the energy transfer rate (MacBride et al. 2005; Sorriso-Valvo et al. 2007; Marino et al. 2008; Smith et al. 2009; Stawarz et al. 2010; Coburn et al. 2012; Bandyopadhyay et al. 2018; Hadid et al. 2018; Andrés et al. 2019; Bandyopadhyay et al. 2020; Sorriso-Valvo et al. 2021).

2016MNRAS.459..277S__Ensslin_et_al._1998_Instance_1

Diffuse synchrotron emission associated with ultrarelativistic particles and magnetic fields in the intracluster medium (ICM) primarily consists of radio haloes and radio relics (see Ferrari et al. 2008; Brüggen et al. 2012; Feretti et al. 2012; Brunetti & Jones 2014 for recent reviews). It is thought that radio haloes are caused by cluster-wide post-merger turbulence (see e.g. Brunetti et al. 2001; Petrosian 2001), secondary electrons from proton–proton interactions (see e.g. Dennison 1980; Blasi & Colafrancesco 1999) or a combination of the two mechanisms (see Brunetti & Blasi 2005; Brunetti & Lazarian 2011; Pinzke, Oh & Pfrommer 2015), whereas radio relics are apparently associated with localized, post-merger shock-fronts (Ensslin et al. 1998). However, the non-detection of gamma-ray emission by the Fermi satellite (see e.g. Brunetti et al. 2012; Zandanel & Ando 2014; Ackermann et al. 2015) disfavours a purely hadronic model for the origin of radio haloes and challenges standard diffuse shock acceleration to explain radio relics (see e.g. Brunetti & Jones 2014; Vazza & Brüggen 2014). Additionally, the variety of the observed properties of cluster-scale radio emission is becoming increasingly difficult to describe within the current theoretical picture. For example: whilst in the ‘Sausage’ cluster (CIZA J2242.8+5301) van Weeren et al. (2010) observe a textbook example of an arc-like radio relic related to a shock; in ZwCl 2341.1+000 Ogrean et al. (2014) observe no X-ray shock at the position of a relic, and in the Bullet cluster (Shimwell et al. 2015), PLCKG287.0+32.9 (Bonafede et al. 2014b) and the Coma cluster (Ensslin et al. 1998) an apparent link is seen between radio galaxies and radio relics. Furthermore, whilst radio haloes are statistically detected in merging clusters (e.g. Cassano et al. 2013), the role of the cluster mass and its dynamical state is difficult to disentangle with current observations (see Cuciti et al. 2015). For example, in CL1821+643 Bonafede et al. (2014a) detect a giant radio halo in a cool core cluster with no obvious merging activity and Russell et al. (2011) observe no diffuse radio emission in Abell 2146, which is a less massive cluster, but a clear merging system. Recently upgraded and new facilities have significantly improved sensitivity to diffuse radio emission from the ICM and are already beginning to reveal increasingly complex phenomena (e.g. Owen et al. 2014) which may shed light on the connection between haloes and relics and further challenge a univocal interpretation of these sources. One such instrument is the Low-Frequency Array (LOFAR; van Haarlem et al. 2013) which can produce deep, high-resolution, high fidelity, low-frequency radio images.

2015MNRAS.450.3458C__Cichowolski_et_al._2001_Instance_2

The kinetic energy stored in the CO shell can be estimated as $E_{\rm kin} = 0.5\, M_{\rm shell}\, V^2_{\rm exp}$, where Vexp is the expansion velocity of the shell and Mshell is the total (molecular, atomic, and ionized) shell mass. Adopting an expansion velocity equal to half the velocity interval where the structure is observed, Vexp = 7.0 ± 1.3 km s− 1 , the molecular mass given in Table 1 and the atomic and ionized masses estimated by Cichowolski et al. (2001), 1450 and 3000 M⊙, respectively, we obtain Ekin = (2.5 ± 1.0) × 1049 erg, assuming a 40 per cent error for the masses.. Although Cichowolski et al. (2001) concluded that WR 130 could have alone created the observed structure, it is important to note that they did not take into account the molecular mass present in the shell, which considerably increases the kinetic shell energy. Thus, we can compare now the new value obtained for Ekin with the mechanical energy deposited in the ISM by the wind of the WR star, Ew = (0.7–2.2) × 1050 erg (Cichowolski et al. 2001). We obtain ϵ = Ekin/Ew = 0.007–0.5. The ratio ϵ measures the energy conversion efficiency in the shell, and according to evolutionary models ϵ ≤ 0.2 (Koo & McKee 1992). Thus, not all the possible values of ϵ are compatible with the scenario where the energy injected during the WR phase is enough to create the structure. In this case, the contribution of the energy injected during the O-star phase and/or other massive stars, should be considered. As mentioned in the Introduction, WR 130 is a WNH star, and according to Smith & Conti (2008) its age would be of about 2–3 Myr and its initial mass of at least 60 M⊙. A rough estimation of the energy injected by such a star during its main sequence yields Ew = (2.5–3.5) × 1050 erg (de Jager, Nieuwenhuijzen & van der Hucht 1988), which would be enough to create the observed structure. We have nevertheless looked for the presence of other massive stars in the region. We queried the available catalogues such as the Galactic O-Star Catalog (Maíz Apellániz et al. 2013), the Early-Type Emission-Line Stars Catalogue (Wackerling 1970), the Catalogue of Be stars (Jaschek & Egret 1982), the H-alpha Stars in the Northern Milky Way Catalogue (Kohoutek & Wehmeyer 1997), and the Catalog of Galactic OB Stars (Reed 2003), for early-type and emission stars. No stars were found in any catalogue. The only massive star located nearby is, as mentioned by Cichowolski et al. (2001), an OB star, which has an uncertain spectral type and no distance estimate (Stock, Nassau & Stephenson 1960). It is located in projection not in the centre of the structure but on to the shell (there is a second OB star mentioned by Cichowolski et al. 2001 but its location is actually outside the structure, see fig. 1 of Cichowolski et al. 2001). Although we cannot completely rule out the possibility that the OB star may be playing a role in creating the shell structure, we think that the action of WR 130 is sufficient and most likely dominant in the region.