Data-driven Discovery of Supernovae in the New Era of Time-Domain Astronomy

Speaker

Daniel Muthukrishna
Massachusetts Institute of Technology (USA)

Abstract

Time-domain astronomy has reached an incredible new era where unprecedented amounts of data are becoming available. New large-scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) are going to revolutionise transient astronomy, providing opportunities to discover entirely new classes of transients while also enabling a deeper understanding of known supernovae. LSST is expected to observe over 10 million transient alerts every night, at least two orders of magnitude more than any preceding survey. In this talk I’ll discuss my development of a new photometric transient classifier, called RAPID (Real-time Automated Photometric IDentification), that is able to automatically classify a range of astronomical transients in real-time. I’ll also discuss the issue that with such large data volumes, the astronomical community will struggle to identify rare and interesting anomalous transients that have previously been found serendipitously. I’ll present two methods of automatically detecting anomalous transient light curves in real-time: one using Temporal Convolutional Neural Networks and the other using a Bayesian parametric model of light curves. Classification and anomaly detection works such as these are vital for enabling fast and prioritised follow-up of transients from upcoming wide-field surveys, as well as increasing the catalog of known supernova useful for cosmology.

Date and Time

March 17 2022
10am KST (= 1am UTC)

Recording

Link to the recording on YouTube