Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ
Published in ApJ, 2025
We present constraints on cosmological and astrophysical parameters using stacked thermodynamic profiles of galaxy clusters from the CAMELS-zoomGZ simulation suite. By combining X-ray and thermal Sunyaev–Zeldovich observables with machine-learning inference methods, we demonstrate that cluster profiles encode significant information about both the cosmological background and the galaxy-formation physics governing feedback processes. Our results show that the combination of stacked profiles from current and upcoming surveys can break degeneracies between cosmological and astrophysical parameters that are problematic for traditional analyses.
Recommended citation: E. Hernández-Martínez, S. Genel, F. Villaescusa-Navarro, U. P. Steinwandel, M. E. Lee, E. T. Lau, D. N. Spergel (2025) "Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ." ApJ. doi:10.3847/1538-4357/ada9e9
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