Hložek, R;
Malz, AI;
Ponder, KA;
Dai, M;
Narayan, G;
Ishida, EEO;
Jr, T Allam;
... Zuo, W; + view all
(2023)
Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC).
The Astrophysical Journal Supplement Series
, 267
(2)
, Article 25. 10.3847/1538-4365/accd6a.
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Abstract
Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
| Type: | Article |
|---|---|
| Title: | Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/1538-4365/accd6a |
| Publisher version: | https://doi.org/10.3847/1538-4365%2Faccd6a |
| Language: | English |
| Additional information: | © 2023 IOP Publishing. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174399 |
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