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UID:pretalx-euroscipy-2026-ZDBNXL@pretalx.com
DTSTART;TZID=CET:20260720T140000
DTEND;TZID=CET:20260720T142000
DESCRIPTION:Galaxy clusters are the largest gravitationally bound structure
 s in the universe\, shaped by both the overall composition of the cosmos a
 nd the complex physics of the gas within them. Disentangling these two inf
 luences is a central challenge in modern astrophysics\, and key to resolvi
 ng cases where different experiments measuring the same fundamental proper
 ties 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 wave
 lengths at unprecedented depth\, making this challenge urgent and tractabl
 e.\n\nWe present a likelihood-free inference framework combining Gaussian 
 process emulation (CARPoolGP) with neural networks to jointly infer 28 cos
 mological 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. Neu
 ral networks\, optimized via Optuna\, map emulated multiwavelength profile
 s 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 2
 8-dimensional parameter space is unprecedented\, allowing us to separate c
 osmology from internal cluster physics\, enabling more reliable cosmologic
 al measurements and better-calibrated simulations.
DTSTAMP:20260603T195654Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:Disentangling Cosmology from Astrophysics with Gaussian Process Emu
 lation and Likelihood-Free Inference - Elena Hernandez-Martinez
URL:https://pretalx.com/euroscipy-2026/talk/ZDBNXL/
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