Data challenges as a tool for time-domain astronomy

TL;DR

Data challenges are crucial for advancing time-domain astronomy, especially in classification and anomaly detection, as large-scale surveys generate vast data requiring innovative evaluation metrics.

Contribution

The paper reviews recent data challenges in time-domain astronomy, focusing on the PLAsTiCC challenge and performance evaluation metrics.

Findings

Highlighting the importance of data challenges in handling large astronomical datasets

Discussion of metrics used for evaluating challenge performance

Emphasis on the role of data challenges in the era of large-scale surveys

Abstract

Data challenges are emerging as powerful tools with which to answer fundamental astronomical questions. Time-domain astronomy lends itself to data challenges, particularly in the era of classification and anomaly detection. With improved sensitivity of wide-field surveys in optical and radio wavelengths from surveys like the Large Synoptic Survey Telescope (LSST) and the Canadian Hydrogen Intensity Mapping Experiment (CHIME), we are entering the large-volume era of transient astronomy. I highlight some recent time-domain challenges, with particular focus on the Photometric LSST Astronomical Time series Classification Challenge (PLAsTiCC), and describe metrics used to evaluate the performance of those entering data challenges.