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SUMMARY:Disentangling Cosmology from Astrophysics with Gaussian Process Em
 ulation and Likelihood-Free Inference - Elena Hernandez-Martinez
DTSTART;TZID=Poland:20260720T140000
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DTSTAMP:20260909T071832Z
UID:pretalx-euroscipy-2026-ZDBNXL@pretalx.com
DESCRIPTION:Galaxy clusters are the largest gravitationally bound structur
 es in the universe\, shaped by both the overall composition of the cosmos 
 and the complex physics of the gas within them. Disentangling these two in
 fluences is a central challenge in modern astrophysics\, and key to resolv
 ing cases where different experiments measuring the same fundamental prope
 rties of the universe yield conflicting results\, pointing to new physics.
 \n\nTelescopes such as eROSITA\, the Simons Observatory\, CMB-S4\, Euclid\
 , and the Rubin Observatory are now observing clusters across multiple wav
 elengths at unprecedented depth\, making this challenge urgent and tractab
 le.\n\nWe present a likelihood-free inference framework combining Gaussian
  process emulation (CARPoolGP) with neural networks to jointly infer 28 co
 smological and astrophysical parameters from stacked cluster profiles. The
  emulator\, trained on 768 hydrodynamic zoom-in simulations (CAMELS-zoomGZ
 )\, generates low-variance predictions across the full parameter space. Ne
 ural networks\, optimized via Optuna\, map emulated multiwavelength profil
 es to posterior moments.\n\nWe achieve correlation coefficients above 0.97
  for all cosmological parameters and above 0.90 for all astrophysical ones
 \, with robustness to realistic noise levels. This accuracy across a full 
 28-dimensional parameter space is unprecedented\, allowing us to separate 
 cosmology from internal cluster physics\, enabling more reliable cosmologi
 cal measurements and better-calibrated simulations.
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/ZDBNXL/
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