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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. 

Abstract We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTFror ATLASobands); a volume-limited sample including all transients within redshiftz< 0.01 (D≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete toz= 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction usingPypeIt, which requires minimal human interaction to ensure reproducibility.