University of Cambridge > Talks.cam > Cosmology Lunch > Explainable deep learning models in cosmology

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Explainable deep learning models in cosmology

Download to your calendar using vCal

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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Castle Teaching Room, Judge Business School CBU Lecture Theatre, 15 Chaucer Road, Cambridge. Zoology

Other talks

Bacterial Physiology During Growth Arrest: Can It Be Targeted by Anti-Bacterial Therapeutics? Title to be confirmed Necessary mechanisms for super AI and stopping hallucinations: The consistent reasoning paradox and the indeterminacy function Security Cooperation within Government and between Governments Talk TBC Shining a light on the origin of supermassive black holes with cosmological simulations
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity