Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)

September 2019 • 2019PASP..131i4501K

Authors • Kessler, R. • Narayan, G. • Avelino, A. • Bachelet, E. • Biswas, R. • Brown, P. J. • Chernoff, D. F. • Connolly, A. J. • Dai, M. • Daniel, S. • Di Stefano, R. • Drout, M. R. • Galbany, L. • González-Gaitán, S. • Graham, M. L. • Hložek, R. • Ishida, E. E. O. • Guillochon, J. • Jha, S. W. • Jones, D. O. • Mandel, K. S. • Muthukrishna, D. • O'Grady, A. • Peters, C. M. • Pierel, J. R. • Ponder, K. A. • Prša, A. • Rodney, S. • Villar, V. A. • LSST Dark Energy Science Collaboration • Transient and Variable Stars Science Collaboration

Abstract • We describe the simulated data sample for the Photometric Large Synoptic Survey Telescope (LSST) Astronomical Time Series Classification Challenge (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the LSST, a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to December 17, and included 1094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of SN Ia used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.

Links

• SIMBAD http://simbad.u-strasbg.fr/simbo.pl?bibcode=2019PASP..131i4501K
• PDF http://iopscience.iop.org/article/10.1088/1538-3873/ab26f1/pdf
• PREPRINT http://arxiv.org/abs/1903.11756
• DATA https://archive.stsci.edu/mastbibref.php?bibcode=2019PASP..131i4501K
• ELECTR http://iopscience.iop.org/article/10.1088/1538-3873/ab26f1