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SUMMARY:Modelling the non-linear Universe with explainable Artificial Inte
 lligence - Hamburg University
DTSTART:20251120T160000Z
DTEND:20251120T170000Z
UID:TALK241036@talks.cam.ac.uk
CONTACT:Matthew Grayling
DESCRIPTION:Precision cosmology is entering a new golden era\, with curren
 t and upcoming surveys mapping the distribution of galaxies to an unpreced
 ented level of detail. However\, uncertainties in the theoretical modellin
 g of the Universe on small\, non-linear scales remain a major roadblock to
  interpreting these cosmological measurements. While machine learning has 
 greatly enhanced our ability to analyse large datasets\, its "black box" n
 ature often limits physical interpretability and trust in their results. I
  will discuss recent advances in modelling cosmological observables in the
  non-linear regime using artificial intelligence (AI)\, and on the impact 
 of baryonic feedback on cosmological observables. I will introduce deep le
 arning frameworks that are explicitly designed to be interpretable and exp
 lainable in terms of the underlying physics of interest\, and demonstrate 
 their application to properties of cosmic structures. I will then present 
 applications to the cosmic microwave background\, revealing to which param
 eters the temperature power spectrum is sensitive in the context of early 
 dark energy models.
LOCATION:Hoyle Lecture Theatre\, Institute of Astronomy
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