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Abstract:
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The Legacy Survey of Space and Time (LSST) on the Rubin Observatory will generate a data deluge: millions of astrophysical transients and variable sources will need to be classified from their time series light curves alone. Photometric classification has long been a problem of interest in the astronomical community, but the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) brings a wide range of models together, simulated under LSST-like observing conditions for the first time. PLAsTiCC was delivered to the community through a Kaggle challenge, designed to stimulate interest in time-series photometric classification and deliver methodologies that will advance the LSST science case. We will give an overview of the road to PLAsTiCC, present and analyze the results of the PLAsTiCC challenge and metrics used to evaluate the challenge, and discuss lessons learned in presenting science-specific challenges to the broader computational and statistical communities.
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