University of Cambridge > Talks.cam > Astro Data Science Discussion Group > From Squiggles to Signals: Learning Useful Representations for Discovery in Time-Domain Astronomy

From Squiggles to Signals: Learning Useful Representations for Discovery in Time-Domain Astronomy

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New large-scale astronomical surveys are observing orders of magnitude more sources than previous surveys, making standard approaches of visually identifying new and interesting phenomena unfeasible. Upcoming surveys such as the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST) and ongoing surveys such as the Transiting Exoplanet Survey Satellite (TESS) have the potential to revolutionize time-domain astronomy, providing opportunities to discover entirely new classes of events while also enabling a deeper understanding of known phenomena. The opportunity for serendipitous discovery in this domain is a new challenge that can be made systematic with data-driven methods, which are particularly suitable for identifying rare and unusual events in large datasets. In this talk, I’ll explore the potential for anomaly detection and representation learning in big datasets, and describe the challenge of applying these methods to real-time surveys. I’ll present novel machine learning methods for automatically detecting anomalous transient events such as kilonovae and peculiar supernovae, and characterising variable stars. I’ll explore the challenge of developing representative latent spaces useful for downstream machine learning tasks and present a novel causally-motivated foundation model. I’ll apply the approach to transients from the Zwicky Transient Facility (ZTF) and simulations of variable stars while discussing applications to upcoming surveys.

This talk is part of the Astro Data Science Discussion Group series.

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