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SUMMARY:Combining simulations and machine learning for a next-generation o
 f mock skies - Zarija Lukic\, Berkeley
DTSTART:20240626T140000Z
DTEND:20240626T143000Z
UID:TALK218401@talks.cam.ac.uk
CONTACT:Niall MacCrann
DESCRIPTION:Ongoing and future cosmology experiments are poised to tackle 
 some of the most profound questions in fundamental physics.  These questio
 ns include explaining the nature of dark energy and dark matter\, and test
 ing particle physics and the theory of gravity on the largest observable s
 cales in the universe.  However\, interpreting the results of these experi
 ments presents a formidable challenge: deducing the underlying physics fro
 m acquired observational data\, or solving an inverse problem.  To rise to
  this challenge in the context of Lyman-alpha forest and intergalactic med
 ium\, we have developed the Nyx code\, demonstrating remarkable efficiency
  and scalability.  Yet\, despite such computational capability\, creating 
 "virtual universes" remains extremely expensive\, especially when high fid
 elity is required.  Over the years\, we have therefore experimented with c
 ombining simulations or physics-driven models with ML-based data-driven mo
 dels\, offering an efficient\, albeit approximate\, alternative to direct 
 simulations.  In this talk\, I will briefly review different approaches we
  have tried and focus on our recent work aimed at improving the accuracy o
 f low-resolution hydrodynamical simulations. This advancement allows us\, 
 for the first time\, to produce simulations in Gpc boxes while maintaining
  accuracy of Lyman-alpha forest predictions comparable to high-resolution 
 hydrodynamical simulations.
LOCATION:Ryle Room\, Kavli
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