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Abstract The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity–stellar mass scaling relation, to investigate how stellar and active galactic nucleus (AGN) feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales (10¹²≲M_halo/M_⊙≲ 10¹³, 10 ≲rkpc⁻¹≲ 400), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All Sky Survey (eRASS) observations, it is found that stronger feedback than is currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match the observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar mass–halo mass relations from observational constraints below the group-mass scale. This tension suggests possible unaccounted-for systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations. 

Abstract 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 Ω_m,H₀, andσ₈, 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_200cto 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 Ω_m,H₀, and the initial mass function 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. 

Abstract We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N -body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io . 

Abstract The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N -body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Ly α spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N -body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io .