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+ ---
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+ task_categories:
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+ - feature-extraction
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+ tags:
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+ - astro
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+ size_categories:
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+ - 1M<n<10M
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+ ---
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+
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+ # Astronomical Time-Series Dataset
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+
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+ This is the full dataset of astronomical time-series from the 2018 Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) Kaggle competition. There are 18 types of astronomical sources represented, including transient phenomena (e.g. supernovae, kilonovae) and variable objects (e.g. active galactic nuclei, Mira variables).
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+
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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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+
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+ ## Dataset Structure
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+
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+ ### Data Fields
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+
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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) for each observation, N=number of observations\
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+ - **target**: 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)\
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+ - **redshift**: true redshift of the object\
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+ - **ddf**: 1 if the object was in the deep drilling fields (DDF) survey area of LSST, 0 if wide-fast-deep (WFD)\
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+ - **hostgal_specz**: spectroscopic redshift of the host galaxy\
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+ - **hostgal_photoz**: photometric redshift of the host galaxy\
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+ - **hostgal_photoz_err**: uncertainty on the photometric redshift
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+
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+ ### Data Splits
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+
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+ The original PLAsTiCC challenge had a training set that was biased to be lower redshift, brighter, and higher signal-to-noise than the test set. This was created to emulate a spectroscopically confirmed subset of observations that typically would be used to train a machine learning classifier. The test set represents a realistic simulation of all LSST observations -- fainter and noisier than the training set. In this dataset, the original PLAsTiCC training set was split into 90/10 training/validation and the original test set was uploaded unchanged.
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+
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+ - **train**: 90% of the PLAsTiCC training set
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+ - **validation**: 10% of the PLAsTiCC training set
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+ - **test**: full PLAsTiCC test set
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+
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+ ## Additional Information
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+
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+ ### Class Descriptions
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+ ```
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+ 6: microlens-single
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+ 15: tidal disruption event (TDE)
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+ 16: eclipsing binary (EB)
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+ 42: type II supernova (SNII)
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+ 52: peculiar type Ia supernova (SNIax)
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+ 53: Mira variable
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+ 62: type Ibc supernova(SNIbc)
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+ 64: kilonova (KN)
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+ 65: M-dwarf
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+ 67: peculiar type Ia supernova (SNIa-91bg)
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+ 88: active galactic nuclei (AGN)
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+ 90: type Ia supernova (SNIa)
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+ 92: RR-Lyrae (RRL)
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+ 95: superluminous supernova (SLSN-I)
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+ 991: microlens-binary
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+ 992: intermediate luminosity optical transient (ILOT)
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+ 993: calcium-rich transient (CaRT)
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+ 994: pair instability supernova (PISN)
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+ 995: microlens-string
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+ ```
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+
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+ ### Citation Information
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+ ```
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+ @ARTICLE{2018arXiv181000001T,
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+ author = {{The PLAsTiCC team} and {Allam}, Tarek, Jr. and {Bahmanyar}, Anita and {Biswas}, Rahul and {Dai}, Mi and {Galbany}, Llu{\'\i}s and {Hlo{\v{z}}ek}, Ren{\'e}e and {Ishida}, Emille E.~O. and {Jha}, Saurabh W. and {Jones}, David O. and {Kessler}, Richard and {Lochner}, Michelle and {Mahabal}, Ashish A. and {Malz}, Alex I. and {Mandel}, Kaisey S. and {Mart{\'\i}nez-Galarza}, Juan Rafael and {McEwen}, Jason D. and {Muthukrishna}, Daniel and {Narayan}, Gautham and {Peiris}, Hiranya and {Peters}, Christina M. and {Ponder}, Kara and {Setzer}, Christian N. and {The LSST Dark Energy Science Collaboration} and {LSST Transients}, The and {Variable Stars Science Collaboration}},
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+ title = "{The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set}",
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+ journal = {arXiv e-prints},
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+ keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
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+ year = 2018,
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+ month = sep,
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+ eid = {arXiv:1810.00001},
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+ pages = {arXiv:1810.00001},
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+ doi = {10.48550/arXiv.1810.00001},
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+ archivePrefix = {arXiv},
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+ eprint = {1810.00001},
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+ primaryClass = {astro-ph.IM},
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+ adsurl = {https://ui.adsabs.harvard.edu/abs/2018arXiv181000001T},
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+ adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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+ }
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+ ```