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Abstract It is important to understand the cycle of baryons through the circumgalactic medium (CGM) in the context of galaxy formation and evolution. In this study, we forecast constraints on the feedback processes heating the CGM with current and future Sunyaev–Zeldovich (SZ) observations. To constrain these processes, we use a suite of cosmological simulations, the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS). CAMELS varies four different feedback parameters of two previously existing hydrodynamical simulations, IllustrisTNG and SIMBA. We capture the dependences of SZ radial profiles on these feedback parameters with an emulator, calculate their derivatives, and forecast future constraints on these feedback parameters from upcoming experiments. We find that for a galaxy sample similar to what would be obtained with the Dark Energy Spectroscopic Instrument at the Simons Observatory, all four feedback parameters can be constrained (some within the 10% level), indicating that future observations will be able to further restrict the parameter space for these subgrid models. Given the modeled galaxy sample and forecasted errors in this work, we find that the inner SZ profiles contribute more to the constraining power than the outer profiles. Finally, we find that, despite the wide range of parameter variation in active galactic feedback in the CAMELS simulation suite, we cannot reproduce the thermal SZ signal of galaxies selected by the Baryon Oscillation Spectroscopic Survey as measured by the Atacama Cosmology Telescope. 

Abstract The thermal Sunyaev–Zel’dovich (tSZ) effect is a powerful tool with the potential for constraining directly the properties of the hot gas that dominates dark matter halos because it measures pressure and thus thermal energy density. Studying this hot component of the circumgalactic medium (CGM) is important because it is strongly impacted by star formation and active galactic nucleus (AGN) activity in galaxies, participating in the feedback loop that regulates star and black hole mass growth in galaxies. We study the tSZ effect across a wide halo-mass range using three cosmological hydrodynamical simulations: Illustris-TNG, EAGLE, and FIRE-2. Specifically, we present the scaling relation between the tSZ signal and halo mass and the (mass-weighted) radial profiles of gas density, temperature, and pressure for all three simulations. The analysis includes comparisons to Planck tSZ observations and to the thermal pressure profile inferred from the Atacama Cosmology Telescope (ACT) measurements. We compare these tSZ data to simulations to interpret the measurements in terms of feedback and accretion processes in the CGM. We also identify as-yet unobserved potential signatures of these processes that may be visible in future measurements, which will have the capability of measuring tSZ signals to even lower masses. We also perform internal comparisons between runs with different physical assumptions. We conclude (1) there is strong evidence for the impact of feedback atR₅₀₀, but that this impact decreases by 5R₅₀₀, and (2) the thermodynamic profiles of the CGM are highly dependent on the implemented model, such as cosmic-ray or AGN feedback prescriptions. 

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 .