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Effective Use of Machine Learning in Astrophysics

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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.

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FW26, William Gates Building, Computer Laboratory Faculty of Education, 184 Hills Road, Cambridge, room 2S4 British Antarctic Survey, Room 187

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