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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 A large fraction of Type Ia supernova (SN Ia) observations over the next decade will be in the near-infrared (NIR), at wavelengths beyond the reach of the current standard light-curve model for SN Ia cosmology, SALT3 (∼2800–8700 Å central filter wavelength). To harness this new SN Ia sample and reduce future light-curve standardization systematic uncertainties, we train SALT3 at NIR wavelengths (SALT3-NIR) up to 2μm with the open-source model-training softwareSALTshaker, which can easily accommodate future observations. Using simulated data, we show that the training process constrains the NIR model to ∼2%–3% across the phase range (−20 to 50 days). We find that Hubble residual (HR) scatter is smaller using the NIR alone or optical+NIR compared to optical alone, by up to ∼30% depending on filter choice (95% confidence). There is significant correlation between NIR light-curve stretch measurements and luminosity, with stretch and color corrections often improving HR scatter by up to ∼20%. For SN Ia observations expected from the Roman Space Telescope, SALT3-NIR increases the amount of usable data in the SALT framework by ∼20% at redshiftz≲ 0.4 and by ∼50% atz≲ 0.15. The SALT3-NIR model is part of the open-sourceSNCosmoandSNANASN Ia cosmology packages. 

Abstract The coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO₂) and releasing nitrous oxide (N₂O) and methane (CH₄). In this second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP2), we quantify global coastal ocean fluxes of CO₂, N₂O and CH₄using an ensemble of global gap‐filled observation‐based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO₂in both observational products and models, but the magnitude of the median net global coastal uptake is ∼60% larger in models (−0.72 vs. −0.44 PgC year⁻¹, 1998–2018, coastal ocean extending to 300 km offshore or 1,000 m isobath with area of 77 million km²). We attribute most of this model‐product difference to the seasonality in sea surface CO₂partial pressure at mid‐ and high‐latitudes, where models simulate stronger winter CO₂uptake. The coastal ocean CO₂sink has increased in the past decades but the available time‐resolving observation‐based products and models show large discrepancies in the magnitude of this increase. The global coastal ocean is a major source of N₂O (+0.70 PgCO₂‐e year⁻¹in observational product and +0.54 PgCO₂‐e year⁻¹in model median) and CH₄(+0.21 PgCO₂‐e year⁻¹in observational product), which offsets a substantial proportion of the coastal CO₂uptake in the net radiative balance (30%–60% in CO₂‐equivalents), highlighting the importance of considering the three greenhouse gases when examining the influence of the coastal ocean on climate.