Effective Use of Machine Learning in Astrophysics

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• Miles Cranmer (Institute of Astronomy)
• Friday 24 November 2023, 11:30-12:30
• Hoyle Lecture Theatre and online (details to be sent by e-mail).

If you have a question about this talk, please contact Steven Brereton.

The field of machine learning (ML) offers a powerful set of frameworks for addressing complex problems in astrophysics, ranging from emulating expensive simulations to performing anomaly detection in large datasets. This talk explores a diverse range of ML applications within astrophysics, highlighting the role of these methods in extracting insights from multidimensional and multimodal datasets. I will also discuss the major challenges of ML, such as model robustness, interpretability, uncertainty estimation, and incorporation of physical priors. In all, this presentation will provide astronomers with a pragmatic overview of machine learning’s capabilities and limitations, and how these techniques will continue to shape astrophysical discovery.

This talk is part of the New Frontiers in Astrophysics: A KICC Perspective series.

This talk is included in these lists:

• Cambridge Astronomy Talks
• Combined External Astrophysics Talks DAMTP
• Cosmology, Astrophysics and General Relativity
• Hoyle Building Lecture Theatre (sign-up needed) + ONLINE - Details will be sent by email
• Hoyle Lecture Theatre and online (details to be sent by e-mail)
• Institute of Astronomy Talk Lists

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