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Abstract We present an analysis of Hubble Space Telescope COS/G160M observations of CIVin the inner circumgalactic medium (CGM) of a novel sample of eightz∼ 0,L≈L^⋆galaxies, paired with UV-bright QSOs at impact parameters (R_proj) between 25 and 130 kpc. The galaxies in this stellar-mass-controlled sample (log₁₀M_⋆/M_⊙∼ 10.2–10.9M_⊙) host supermassive black holes (SMBHs) with dynamically measured masses spanning log₁₀M_BH/M_⊙∼ 6.8–8.4; this allows us to compare our results with models of galaxy formation where the integrated feedback history from the SMBH alters the CGM over long timescales. We find that the CIVcolumn density measurements (N_{C IV}; average log₁₀N_{C IV,CH}= 13.94 ± 0.09 cm⁻²) are largely consistent with existing measurements from other surveys ofN_{C IV}in the CGM (average log₁₀N_{C IV,Lit}= 13.90 ± 0.08 cm⁻²), but do not show obvious variation as a function of the SMBH mass. By contrast, specific star formation rate (sSFR) is highly correlated with the ionized content of the CGM. We find a large spread in sSFR for galaxies with log₁₀M_BH/M_⊙> 7.0, where the CGM CIVcontent shows a clear dependence on galaxy sSFR but notM_BH. Our results do not indicate an obvious causal link between CGM CIVand the mass of the galaxy’s SMBH; however, through comparisons to the EAGLE, Romulus25, and IllustrisTNG simulations, we find that our sample is likely too small to constrain such causality. 

ABSTRACT The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large data sets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy’s CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (H i) radio two-dimensional maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ‘multifield’ data sets comprised of both H i and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual haloes with masses log (Mhalo/M⊙) < 12.5. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis – training on one galaxy formation model and testing on another – highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on the physical properties of CGM depends on our ability to interpret these deep-learning models. 

Abstract Quenching of star formation in the central galaxies of cosmological halos is thought to result from energy released as gas accretes onto a supermassive black hole. The same energy source also appears to lower the central density and raise the cooling time of baryonic atmospheres in massive halos, thereby limiting both star formation and black hole growth, by lifting the baryons in those halos to greater altitudes. One predicted signature of that feedback mechanism is a nearly linear relationship between the central black hole’s mass (M_BH) and the original binding energy of the halo’s baryons. We present the increasingly strong observational evidence supporting a such a relationship, showing that it extends up to halos of massM_halo∼ 10¹⁴M_⊙. We then compare current observational constraints on theM_BH–M_halorelation with numerical simulations, finding that black hole masses in IllustrisTNG appear to exceed those constraints atM_halo< 10¹³M_⊙and that black hole masses in EAGLE fall short of observations atM_halo∼ 10¹⁴M_⊙. A closer look at IllustrisTNG shows that quenching of star formation and suppression of black hole growth do indeed coincide with black hole energy input that lifts the halo’s baryons. However, IllustrisTNG does not reproduce the observedM_BH–M_halorelation because its black holes gain mass primarily through accretion that does not contribute to baryon lifting. We suggest adjustments to some of the parameters in the IllustrisTNG feedback algorithm that may allow the resulting black hole masses to reflect the inherent links between black hole growth, baryon lifting, and star formation among the massive galaxies in those simulations. 

ABSTRACT Many phenomenologically successful cosmological simulations employ kinetic winds to model galactic outflows. Yet systematic studies of how variations in kinetic wind scalings might alter observable galaxy properties are rare. Here we employ gadget-3 simulations to study how the baryon cycle, stellar mass function, and other galaxy and CGM predictions vary as a function of the assumed outflow speed and the scaling of the mass-loading factor with velocity dispersion. We design our fiducial model to reproduce the measured wind properties at 25 per cent of the virial radius from the Feedback In Realistic Environments simulations. We find that a strong dependence of η ∼ σ5 in low-mass haloes with $$\sigma \lt 106\mathrm{\, km\, s^{-1}}$$ is required to match the faint end of the stellar mass functions at $$z$$ > 1. In addition, faster winds significantly reduce wind recycling and heat more halo gas. Both effects result in less stellar mass growth in massive haloes and impact high ionization absorption in halo gas. We cannot simultaneously match the stellar content at $$z$$ = 2 and 0 within a single model, suggesting that an additional feedback source such as active galactic nucleus might be required in massive galaxies at lower redshifts, but the amount needed depends strongly on assumptions regarding the outflow properties. We run a 50 $$\mathrm{Mpc}\, h^{-1}$$, 2 × 5763 simulation with our fiducial parameters and show that it matches a range of star-forming galaxy properties at $$z$$ ∼ 0–2. 

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 .