Data challenges as a tool for time-domain astronomy

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.

Accepted for publication in the Publications of the Astronomical Society of the Pacific as part of a 'Focus on Tools and Techniques for Time-domain Astronomy'

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Cited by in corpus (4)

• Galaxy classification: deep learning on the OTELO and COSMOS databases
• SPARKESX: Single-dish PARKES data sets for finding the uneXpected -- A data challenge
• FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy
• Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning