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Known as the ‘Missing Baryon Problem’, about one-third of baryons in the local universe remain unaccounted for. The missing baryons are thought to reside in the warm–hot intergalactic medium (WHIM) of the cosmic web filaments, which are challenging to detect. Recent Chandra X-ray observations used a novel stacking analysis and detected an O vii absorption line towards the sightline of a luminous quasar, hinting that the missing baryons may reside in the WHIM. To explore how the properties of the O vii absorption line depend on feedback physics, we compare the observational results with predictions obtained from the Cosmology and Astrophysics with MachinE Learning (CAMEL) Simulation suite. CAMELS consists of cosmological simulations with state-of-the-art supernova (SN) and active galactic nuclei (AGNs) feedback models from the IllustrisTNG and SIMBA simulations, with varying strengths. We find that the simulated O vii column densities are higher in the outskirts of galaxies than in the large-scale WHIM, but they are consistently lower than those obtained in the Chandra observations, for all feedback runs. We establish that the O vii distribution is primarily sensitive to changes in the SN feedback prescription, whereas changes in the AGN feedback prescription have minimal impact. We also find significant differences in the O vii column densities between the IllustrisTNG and SIMBA runs. We conclude that the tension between the observed and simulated O vii column densities cannot be explained by the wide range of feedback models implemented in CAMELS.

 

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 . 

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 . 

We use a statistical sample of galaxy clusters from a large cosmological N-body + hydrodynamics simulation to examine the relation between morphology, or shape, of the X-ray emitting intracluster medium (ICM) and the mass accretion history of the galaxy clusters. We find that the mass accretion rate (MAR) of a cluster is correlated with the ellipticity of the ICM. The correlation is largely driven by material accreted in the last ∼4.5 Gyr, indicating a characteristic time-scale for relaxation of cluster gas. Furthermore, we find that the ellipticity of the outer regions (R ∼ R500c) of the ICM is correlated with the overall MAR of clusters, while ellipticity of the inner regions (≲0.5 R500c) is sensitive to recent major mergers with mass ratios of ≥1:3. Finally, we examine the impact of variations in cluster mass accretion history on the X-ray observable–mass scaling relations. We show that there is a continuous anticorrelation between the residuals in the TX–M relation and cluster MARs, within which merging and relaxed clusters occupy extremes of the distribution rather than form two peaks in a bimodal distribution, as was often assumed previously. Our results indicate that the systematic uncertainties in the X-ray observable–mass relations can be mitigated by using the information encoded in the apparent ICM ellipticity.