STAMPS@CMU presents:

Time-domain Astrophysics in the Era of Big Data

by Ashley Villar (Harvard)

Online webinar February 16, 2024 at 1:30-2:30 PM ET.
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The eruptions, collisions and explosions of stars drive the universe’s chemical and dynamical evolution. The upcoming Legacy Survey of Space and Time will drastically increase the discovery rate of these transient phenomena, bringing time-domain astrophysics into the realm of “big data.” With this transition comes the important question: how do we classify transient events and separate the interesting “needles” from the “haystack” of objects? In this talk, I will discuss efforts to discover and classify unexpected phenomena using semi-supervised machine learning techniques. I will highlight the interplay between data-informed physics and physics-informed machine learning required to best understand the future LSST dataset of extragalactic transients.


Ashley Villar is an assistant professor of Astronomy at Harvard University. Her research focuses on data-driven analysis of optical transients, including core-collapse supernovae and kilonovae. She is particularly interested in representation learning for sparse, multivariate light curves. Ashley is the co-Chair of the LSST Informatics and Statistics Science.