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A systematic comparison of the models of the circumgalactic medium (CGM) and their observables is crucial to understanding the predictive power of the models and constraining physical processes that affect the thermodynamics of CGM. This paper compares four analytic CGM models: precipitation, isentropic, cooling flow, and baryon pasting models for the hot, volume-filling CGM phase, all assuming hydrostatic or quasi-hydrostatic equilibrium. We show that for fiducial parameters of the CGM of a Milky Way (MW)-like galaxy ($M_{\rm vir} \sim 10^{12}~{\rm M}_{\odot }$ at $z\sim 0$), the thermodynamic profiles – entropy, density, temperature, and pressure – show most significant differences between different models at small ($r\lesssim 30$ kpc) and large scales ($r\gtrsim 100$ kpc) while converging at intermediate scales. The slope of the entropy profile, which is one of the most important differentiators between models, is $\approx 0.8$ for the precipitation and cooling flow models, while it is $\approx 0.6$ and 0 for the baryon pasting and isentropic models, respectively. We make predictions for various observational quantities for an MW mass halo for the different models, including the projected Sunyaev–Zeldovich effect, soft X-ray emission (0.5–2 keV), dispersion measure, and column densities of oxygen ions (O vi, O vii, and O viii) observable in absorption. We provide Python packages to compute the thermodynamic and observable quantities for the different CGM models.

We explore the evolution of cold streams from the cosmic web that feed galaxies through their shock-heated circumgalactic medium (CGM) at cosmic noon, $z\simeq 1-5$. In addition to the hydrodynamical instabilities and radiative cooling that we have incorporated in earlier works, we embed the stream and the hot CGM in the gravitational potential of the host dark matter halo, deriving equilibrium profiles for both. Self-gravity within the stream is tentatively ignored. We find that the cold streams gradually entrain a large mass of initially hot CGM gas that cools in the mixing layer and condenses onto the stream. This entrainment, combined with the acceleration down the gravitational potential well, typically triples the inward cold inflow rate into the central galaxy, compared to the original rate at the virial radius, which makes the entrained gas the dominant source of gas supply to the galaxy. The potential sources for the hot gas to be entrained are recycled enriched gas that has been previously ejected from the galaxy, and fresh virial-shock-heated gas that has accumulated in the CGM. This can naturally elevate the star formation rate in the galaxy by a factor of $\sim 3$ compared to the gas accretion rate onto the halo, thus explaining the otherwise puzzling observed excess of star formation at cosmic noon. When accounting for self-shielding of dense gas from the ultraviolet background, we find that the energy radiated from the streams, originating predominantly from the cooling of the entrained gas, is consistent with observed Lyman-$\alpha$ blobs around galaxies.

Most diffuse baryons, including the circumgalactic medium (CGM) surrounding galaxies and the intergalactic medium (IGM) in the cosmic web, remain unmeasured and unconstrained. Fast radio bursts (FRBs) offer an unparalleled method to measure the electron dispersion measures (DMs) of ionized baryons. Their distribution can resolve the missing baryon problem and constrain the history of feedback theorized to impart significant energy to the CGM and IGM. We analyze the Cosmology and Astrophysics with Machine Learning Simulations using three suites, IllustrisTNG, SIMBA, and Astrid, each varying six parameters (two cosmological and four astrophysical feedback), for a total of 183 distinct simulation models. We find significantly different predictions between the fiducial models of the suites owing to their different implementations of feedback. SIMBA exhibits the strongest feedback, leading to the smoothest distribution of baryons and reducing the sight-line-to-sight-line variance in DMs betweenz= 0 and 1. Astrid has the weakest feedback and the largest variance. We calculate FRB CGM measurements as a function of galaxy impact parameter, with SIMBA showing the weakest DMs due to aggressive active galactic nucleus (AGN) feedback and Astrid the strongest. Within each suite, the largest differences are due to varying AGN feedback. IllustrisTNG shows the most sensitivity to supernova feedback, but this is due to the change in the AGN feedback strengths, demonstrating that black holes, not stars, are most capable of redistributing baryons in the IGM and CGM. We compare our statistics directly to recent observations, paving the way for the use of FRBs to constrain the physics of galaxy formation and evolution.

We present a novel approach for identifying cosmic web filaments within theDisPerSEstructure identification framework, using cosmic density field estimates from the Monte Carlo Physarum Machine (MCPM), inspired by the slime mold organism. We apply our method to the IllustrisTNG (TNG100) cosmological simulations and investigate the impact of filaments on galaxies. The MCPM density field is superior to the Delaunay tessellation field estimator in tracing the true underlying matter distribution and allows filaments to be identified with higher fidelity, finding more low-prominence/diffuse filaments. Using our new filament catalogs, we find that ≳90% of galaxies are located within ∼1.5 Mpc of a filamentary spine, with little change in the median star formation activity with distance to the nearest filament. Instead, we uncover a differential effect of the local filament line density, Σ_fil(MCPM)—the total MCPM overdensity per unit length along a filament segment—on galaxy formation: most galaxies are quenched and gas-poor near high-line density filaments atz≤ 1. At earlier times, the filamentary environment appears to have no effect on galactic gas supply and quenching. Atz= 0, quenching in$\log (M_{*}/M_{\odot })\gtrsim 10.5$galaxies is mainly driven by mass, while lower-mass galaxies are significantly affected by the filament line density. Satellites are far more susceptible to filaments than centrals. The local environments of massive halos are not sufficient to account for the effect of filament line density on gas removal and quenching. Our new approach holds great promise for observationally identifying filaments from galaxy surveys such as SDSS and DESI.

A narrow linear object extending ∼60 kpc from the centre of a galaxy at redshift z ∼ 1 has recently been discovered and interpreted as shocked gas filament forming stars. The host galaxy presents an irregular morphology, implying recent merger events. Supposing that each of the progenitor galaxies has a central supermassive black hole (SMBH) and the SMBHs are accumulated at the centre of the merger remnant, a fraction of them can be ejected from the galaxy with a high velocity due to interactions between SMBHs. When such a runaway SMBH (RSMBH) passes through the circumgalactic medium (CGM), converging flows are induced along the RSMBH path, and star formation could eventually be ignited. We show that the CGM temperature prior to the RSMBH perturbation should be below the peak temperature in the cooling function to trigger filament formation. While the gas is temporarily heated due to compression, the cooling efficiency increases, and gas accumulation becomes allowed along the path. When the CGM density is sufficiently high, the gas can cool down and develop a dense filament by z = 1. The mass and velocity of the RSMBH determine the scale of filament formation. Hydrodynamical simulations validate the analytical expectations. Therefore, we conclude that the perturbation by RSMBHs is a viable channel to form the observed linear object. Using the analytical model validated by simulations, we show that the CGM around the linear object to be warm ($T \lesssim 2 \times 10^5$ K) and dense ($n \gtrsim 2 \times 10^{-5} (T/2 \times 10^5 \, K)^{-1} \, {\rm cm^{-3}}$).

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with nontrivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, in particular for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method,CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low-variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with machinE Learning Simulations to include a set of 768 zoom-in simulations of halos in the mass range of 10¹³–10^14.5M_⊙h⁻¹that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the ComptonY–M, black hole mass–halo mass, and metallicity–mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of theY–Mrelation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev–Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package.

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.

Galaxy cluster mergers are rich sources of information to test cluster astrophysics and cosmology. However, cluster mergers produce complex projected signals that are difficult to interpret physically from individual observational probes. Multi-probe constraints on the gas and dark matter (DM) cluster components are necessary to infer merger parameters that are otherwise degenerate. We present Improved Constraints on Mergers with SZ, Hydrodynamical simulations, Optical, and X-ray (ICM-SHOX), a systematic framework to jointly infer multiple merger parameters quantitatively via a pipeline that directly compares a novel combination of multi-probe observables to mock observables derived from hydrodynamical simulations. We report a first application of the ICM-SHOX pipeline to MACS J0018.5+1626, wherein we systematically examine simulated snapshots characterized by a wide range of initial parameters to constrain the MACS J0018.5+1626 merger geometry. We constrain the epoch of MACS J0018.5+1626 to the range 0–60 Myr post-pericenter passage, and the viewing angle is inclined ≈27°–40° from the merger axis. We obtain constraints for the impact parameter (≲250 kpc), mass ratio (≈1.5–3.0), and initial relative velocity when the clusters are separated by 3 Mpc (≈1700–3000 km s⁻¹). The primary and secondary clusters initially (at 3 Mpc) have gas distributions that are moderately and strongly disturbed, respectively. We discover a velocity space decoupling of the DM and gas distributions in MACS J0018.5+1626, traced by cluster-member galaxy velocities and the kinematic Sunyaev–Zel'dovich effect, respectively. Our simulations indicate this decoupling is dependent on the different collisional properties of the two distributions for particular merger epochs, geometries, and viewing angles.

We introduce a new model of the evolution of the concentration of dark matter haloes, c(t). For individual haloes, our model approximates c(t) as a power law with a time-dependent index, such that at early times, concentration has a nearly constant value of c ≈ 3–4, and as cosmic time progresses, c(t) smoothly increases. Using large samples of halo merger trees taken from the Bolshoi–Planck and MultiDark Planck 2 cosmological simulations, we demonstrate that our three-parameter model can approximate the evolution of the concentration of individual haloes with a typical accuracy of 0.1 dex for $t\gtrsim 2\, {\rm Gyr}$ for all Bolshoi–Planck and MultiDark Planck 2 haloes of present-day peak mass $M_{0}\gtrsim 10^{11.5}\, {\rm M}_{\odot }$. We additionally present a new model of the evolution of the concentration of halo populations, which we show faithfully reproduces both average concentration growth and the diversity of smooth trajectories of c(t), including capturing correlations with halo mass and halo assembly history. Our publicly available source code, diffprof, can be used to generate Monte Carlo realizations of the concentration histories of cosmologically representative halo populations. diffprof is differentiable due to its implementation in the jax autodiff library, which facilitates the incorporation of our model into existing analytical halo model frameworks.

We quantify the cosmological spread of baryons relative to their initial neighbouring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighbouring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighbouring dark matter owing to feedback from supernovae (SNe) and active galactic nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial simba model spreading ∼40 per cent of baryons >1 Mpc away compared to ∼10 per cent for the IllustrisTNG and astrid models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and (to a lesser extent) galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to $k \sim 10\, h$ Mpc−1 in SIMBA while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.