The eruptions collisions and explosions of stars drive the universe’s chemical and dynamical evolution. The upcoming Large Synoptic Survey Telescope 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.
Biography:
Ashley Villar is currently an Assistant Professor at the Pennsylvania State University. She was previously a Simons Junior Fellow at Columbia University and the Flatiron Institute. Professor Villar obtained her Ph.D. in Astronomy & Astrophysics from Harvard in 2020 and her Batchelor’s in Physics and Maths from MIT.
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