Speaker
Description
The next generation of telescopes such as the SKA and the Vera C. Rubin Observatory will produce enormous data sets, far too large for traditional analysis techniques. Machine learning has proven invaluable in handling massive data volumes and automating many tasks traditionally done by human scientists. In this talk, I will explore the use of machine learning for automating the discovery and follow-up of interesting astronomical phenomena, both in the image and time domains. I will discuss how the human-machine interface plays a critical role in maximising scientific discovery with automated tools, demonstrating applications of the active anomaly detection framework, Astronomaly, on a variety of datasets. Finally, I will investigate the role foundation models play in enabling scientific discovery in massive surveys.
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