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--- |
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license: mit |
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tags: |
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- time series |
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- astrophysics |
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- pretraining |
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- connect-later |
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size_categories: |
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- 100K<n<1M |
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--- |
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# AstroClassification and Redshifts Datasets |
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<!-- Provide a quick summary of the dataset. --> |
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This dataset was used for the AstroClassification and Redshifts introduced in [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](). This is a dataset of simulated astronomical time-series (e.g., supernovae, active galactic nuclei), and the task is to classify the object type (AstroClassification) or predict the object's redshift (Redshifts). |
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- **Repository:** https://github.com/helenqu/connect-later |
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- **Paper:** will be updated |
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- **Point of Contact: Helen Qu (<helenqu@sas.upenn.edu>)** |
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## Dataset Structure |
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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- **object_id**: unique object identifier |
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- **times_wv**: 2D array of shape (N, 2) containing the observation times (modified Julian days, MJD) and filter (wavelength in nm) for each observation, N=number of observations |
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- **lightcurve**: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation |
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- **label**: integer representing the class of the object (see below for details) |
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- **redshift**: redshift of the object |
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## Dataset Creation |
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### Source Data |
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This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition |
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The original Kaggle competition can be found [here](https://www.kaggle.com/c/PLAsTiCC-2018). [This note](https://arxiv.org/abs/1810.00001) from the competition describes the dataset in detail. Astronomers may be interested in [this paper](https://arxiv.org/abs/1903.11756) describing the simulations used to generate the data. |
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- **Train**: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper |
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- **Validation**: Remaining 20% of the original PLAsTiCC training set, *not* augmented or modified |
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- **Test**: Subset of 10,000 objects randomly selected from the PLAsTiCC test set |
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### Object Types |
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``` |
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0: microlens-single |
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1: tidal disruption event (TDE) |
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2: eclipsing binary (EB) |
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3: type II supernova (SNII) |
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4: peculiar type Ia supernova (SNIax) |
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5: Mira variable |
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6: type Ibc supernova(SNIbc) |
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7: kilonova (KN) |
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8: M-dwarf |
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9: peculiar type Ia supernova (SNIa-91bg) |
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10: active galactic nuclei (AGN) |
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11: type Ia supernova (SNIa) |
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12: RR-Lyrae (RRL) |
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13: superluminous supernova (SLSN-I) |
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14: 5 "anomalous" types that are not present in training set: microlens-binary, intermediate luminosity optical transient (ILOT), calcium-rich transient (CaRT), pair instability supernova (PISN), microlens-string |
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``` |
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## Citation |
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will be updated |
