Explainable deep learning models in cosmology

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• Luisa Lucie-Smith (University of Hamburg)
• Monday 17 February 2025, 13:00-14:00
• CMS, Pav. B, CTC Common Room (B1.19) [Potter Room].

If you have a question about this talk, please contact Thomas Colas.

Machine learning has significantly improved the way cosmologists model and interpret cosmological data; yet, its “black box” nature often limits our ability to trust and understand its results. In this talk, I will present an explainable deep learning framework designed to rely on a minimal set of physically interpretable parameters which describe the data. I will first discuss applications to dark matter halos, demonstrating how these neural networks can be used to model their final properties — such as their density profiles — and connect them to the underlying physics. Additionally, I will present applications to the cosmic microwave background, revealing to which parameters the CMB temperature power spectrum is sensitive in the context of early dark energy models.

This talk is part of the Cosmology Lunch series.

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