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Next-generation surveys like the Legacy Survey of Space and Time (LSST) onthe Vera C. Rubin Observatory will generate orders of magnitude morediscoveries of transients and variable stars than previous surveys. To preparefor this data deluge, we developed the Photometric LSST AstronomicalTime-series Classification Challenge (PLAsTiCC), a competition which aimed tocatalyze the development of robust classifiers under LSST-like conditions of anon-representative training set for a large photometric test set of imbalancedclasses. Over 1,000 teams participated in PLAsTiCC, which was hosted in theKaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018,ultimately identifying three winners in February 2019. Participants producedclassifiers employing a diverse set of machine learning techniques includinghybrid combinations and ensemble averages of a range of approaches, among themboosted decision trees, neural networks, and multi-layer perceptrons. Thestrong performance of the top three classifiers on Type Ia supernovae andkilonovae represent a major improvement over the current state-of-the-artwithin astronomy. This paper summarizes the most promising methods andevaluates their results in detail, highlighting future directions both forclassifier development and simulation needs for a next generation PLAsTiCC dataset.

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)