Using Machine Learning for Discovery in Synoptic Survey Imaging

Understand

Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability.

• Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are highly outnumbered by bogus detections caused by imperfect subtractions, atmospheric effects and detector artefacts.
• In this work we present a machine learning (ML) framework for discovery of variability in time-domain imaging surveys.
• Our ML methods provide probabilistic statements, in near real time, about the degree to which each newly observed source is astrophysically relevant source of variable brightness.