Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
Paper
• 1903.11756 • Published
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in Data Studio
AstroClassification and Redshifts Datasets
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).
- Repository: https://github.com/helenqu/connect-later
- Paper: will be updated
- Point of Contact: Helen Qu (helenqu@sas.upenn.edu)
Dataset Structure
- object_id: unique object identifier
- 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
- lightcurve: 2D array of shape (N, 2) containing the flux (arbitrary units) and flux error for each observation
- label: integer representing the class of the object (see below for details)
- redshift: redshift of the object
Dataset Creation
Source Data
This is a modified version of the dataset from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition The original Kaggle competition can be found here. This note from the competition describes the dataset in detail. Astronomers may be interested in this paper describing the simulations used to generate the data.
- Train: 80% of the original PLAsTiCC training set augmented using the redshifting targeted augmentation described in the Connect Later paper
- Validation: Remaining 20% of the original PLAsTiCC training set, not augmented or modified
- Test: Subset of 10,000 objects randomly selected from the PLAsTiCC test set
Object Types
0: microlens-single
1: tidal disruption event (TDE)
2: eclipsing binary (EB)
3: type II supernova (SNII)
4: peculiar type Ia supernova (SNIax)
5: Mira variable
6: type Ibc supernova(SNIbc)
7: kilonova (KN)
8: M-dwarf
9: peculiar type Ia supernova (SNIa-91bg)
10: active galactic nuclei (AGN)
11: type Ia supernova (SNIa)
12: RR-Lyrae (RRL)
13: superluminous supernova (SLSN-I)
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
Citation
will be updated
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Size of the auto-converted Parquet files (First 5GB per split):
1.52 GB
Number of rows (First 5GB per split):
433,036