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Classification of transient and variable light curves is an essential step inusing astronomical observations to develop an understanding of their underlyingphysical processes. However, upcoming deep photometric surveys, including theLarge Synoptic Survey Telescope (LSST), will produce a deluge of lowsignal-to-noise data for which traditional labeling procedures areinappropriate. Probabilistic classification is more appropriate for the databut are incompatible with the traditional metrics used on deterministicclassifications. Furthermore, large survey collaborations intend to use theseclassification probabilities for diverse science objectives, indicating a needfor a metric that balances a variety of goals. We describe the process used todevelop an optimal performance metric for an open classification challenge thatseeks probabilistic classifications and must serve many scientific interests.The Photometric LSST Astronomical Time-series Classification Challenge(PLAsTiCC) is an open competition aiming to identify promising techniques forobtaining classification probabilities of transient and variable objects byengaging a broader community both within and outside astronomy. Using mockclassification probability submissions emulating archetypes of thoseanticipated of PLAsTiCC, we compare the sensitivity of metrics ofclassification probabilities under various weighting schemes, finding that theyyield qualitatively consistent results. We choose as a metric for PLAsTiCC aweighted modification of the cross-entropy because it can be meaningfullyinterpreted. Finally, we propose extensions of our methodology to ever morecomplex challenge goals and suggest some guiding principles for approaching thechoice of a metric of probabilistic classifications.

the astronomical journal

The Photometric LSST Astronomical Time-series Classification Challenge  (PLAsTiCC): Selection of a performance metric for classification  probabilities balancing diverse science goals