Abstract
Abstract Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products.
Highlights
The Large Synoptic Survey Telescope (LSST) will revolutionize time-domain astronomy and the study of transient and variable objects within and beyond the Milky Way
Though photometric light curves like those of LSST can be used for classification, costly observations of a high-resolution spectrum have traditionally served as the gold standard for classification
There is an acute need for classifiers of photometric light curves that can perform well on data sets that include a wide variety of sources, including those that are at the limits of detection
Summary
The Large Synoptic Survey Telescope (LSST) will revolutionize time-domain astronomy and the study of transient and variable objects within and beyond the Milky Way. With its rapid scan strategy, exquisite depth, and multiple optical filters, LSST will deliver millions of light curves, comprised of timeseries observations in six electromagnetic wavelength ranges divided into photometric bands in the visible regime. LSST’s expansive catalog of light curves will enable unprecedented population-level studies of time-varying astrophysical sources, from asteroids to variable stars to active galactic nuclei, deepening our understanding of stellar aging processes, the evolution of the most massive galaxies, and the expansion history of the universe, to name but a few. There is an acute need for classifiers of photometric light curves that can perform well on data sets that include a wide variety of sources, including those that are at the limits of detection
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