Kavli Affiliate: David N. Spergel

| First 5 Authors: Elena Hernández-Martínez, Shy Genel, Francisco Villaescusa-Navarro, Ulrich P. Steinwandel, Max E. Lee

| Summary:

We present a study on the inference of cosmological and astrophysical
parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ
simulations, we explore how various cluster properties–such as X-ray surface
brightness, gas density, temperature, metallicity, and Compton-y profiles–can
be used to predict parameters within the 28-dimensional parameter space of the
IllustrisTNG model. Through neural networks, we achieve a high correlation
coefficient of 0.97 or above for all cosmological parameters, including
$Omega_{rm m}$, $H_0$, and $sigma_8$, and over 0.90 for the remaining
astrophysical parameters, showcasing the effectiveness of these profiles for
parameter inference. We investigate the impact of different radial cuts, with
bins ranging from $0.1R_{200c}$ to $0.7R_{200c}$, to simulate current
observational constraints. Additionally, we perform a noise sensitivity
analysis, adding up to 40% Gaussian noise (corresponding to signal-to-noise
ratios as low as 2.5), revealing that key parameters such as $Omega_{rm m}$,
$H_0$, and the IMF slope remain robust even under extreme noise conditions. We
also compare the performance of full radial profiles against integrated
quantities, finding that profiles generally lead to more accurate parameter
inferences. Our results demonstrate that stacked galaxy cluster profiles
contain crucial information on both astrophysical processes within groups and
clusters and the underlying cosmology of the universe. This underscores their
significance for interpreting the complex data expected from next-generation
surveys and reveals, for the first time, their potential as a powerful tool for
parameter inference.

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