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Abstract Recent works have discovered a relatively tight correlation between Ω_mand the properties of individual simulated galaxies. Because of this, it has been shown that constraints on Ω_mcan be placed using the properties of individual galaxies while accounting for uncertainties in astrophysical processes such as feedback from supernovae and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters (Ω_mandσ₈) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the Ω_minference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on Ω_mfrom one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models. 

ABSTRACT In recent years, cosmological hydrodynamical simulations have proven their utility as key interpretative tools in the study of galaxy formation and evolution. In this work, we present a comparative analysis of the baryon cycle in three publicly available, leading cosmological simulation suites: EAGLE, IllustrisTNG, and SIMBA. While these simulations broadly agree in terms of their predictions for the stellar mass content and star formation rates of galaxies at $$z\approx 0$$, they achieve this result for markedly different reasons. In EAGLE and SIMBA, we demonstrate that at low halo masses ($$M_{\rm 200c}\lesssim 10^{11.5}\, \mathrm{M}_{\odot }$$), stellar feedback (SF)-driven outflows can reach far beyond the scale of the halo, extending up to $$2\!-\!3\times R_{\rm 200c}$$. In contrast, in TNG, SF-driven outflows, while stronger at the scale of the interstellar medium, recycle within the circumgalactic medium (within $$R_{\rm 200c}$$). We find that active galactic nucleus (AGN)-driven outflows in SIMBA are notably potent, reaching several times $$R_{\rm 200c}$$ even at halo masses up to $$M_{\rm 200c}\approx 10^{13.5}\, \mathrm{M}_{\odot }$$. In both TNG and EAGLE, AGN feedback can eject gas beyond $$R_{\rm 200c}$$ at this mass scale, but seldom beyond $$2\!-\!3\times R_{\rm 200c}$$. We find that the scale of feedback-driven outflows can be directly linked with the prevention of cosmological inflow, as well as the total baryon fraction of haloes within $$R_{\rm 200c}$$. This work lays the foundation to develop targeted observational tests that can discriminate between feedback scenarios, and inform subgrid feedback models in the next generation of simulations. 

Abstract 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. 

Abstract 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. 

ABSTRACT 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. 

Abstract Galaxies that are invisible in deep optical–near-infrared imaging but detected at longer wavelengths have been the focus of several recent observational studies, with speculation that they could constitute a substantial missing population and even dominate the cosmic star formation rate density atz≳ 4. The depths now achievable with JWST at the longest wavelengths probed by the Hubble Space Telescope (HST), coupled with the transformative resolution at longer wavelengths, are already enabling detailed, spatially resolved characterization of sources that were invisible to HST, often known as “HST-dark” galaxies. However, until now, there has been little theoretical work to compare against. We present the first simulation-based study of this population, using highly resolved galaxies from the Feedback in Realistic Environments project, with multiwavelength images along several lines of sight forward-modeled using radiative transfer. We naturally recover a population of modeled sources that meet commonly used selection criteria (H_AB> 27 mag andH_AB− F444W > 2.3). These simulated HST-dark galaxies lie at high redshifts (z= 4–7), have high levels of dust attenuation (A_V= 2–4), and display compact recent star formation (R_1/2,4.4μm≲ 1 kpc). Orientation is very important: for all but one of the 17 simulated galaxy snapshots with HST-dark sight lines, there exist other sight lines that do not meet the criteria. This result has important implications for comparisons between observations and models that do not resolve the detailed star-dust geometry, such as semianalytic models or coarsely resolved hydrodynamical simulations. Critically, we demonstrate that HST-dark sources are not an unexpected or exotic population, but a subset of high-redshift, highly dust-attenuated sources viewed along certain lines of sight. 

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 We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω_m,σ₈, Ω_b) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set. 

Abstract We explore the role of galactic feedback on the low-redshift Lyα(Lyα) forest (z≲ 2) statistics and its potential to alter the thermal state of the intergalactic medium. Using the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) suite, we explore variations of the AGN and stellar feedback models in the IllustrisTNG and Simba subgrid models. We find that both AGN and stellar feedback in Simba play a role in setting the Lyαforest column density distribution function (CDD) and the Doppler width (b-value) distribution. The Simba AGN jet feedback mode is able to efficiently transport energy out to the diffuse IGM, causing changes in the shape and normalization of the CDD and a broadening of theb-value distribution. We find that stellar feedback plays a prominent role in regulating supermassive black hole growth and feedback, highlighting the importance of constraining stellar and AGN feedback simultaneously. In IllustrisTNG, the AGN feedback variations explored in CAMELS do not affect the Lyαforest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-zLyαforest can be sensitive to changes in the ultraviolet background, stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM. 

ABSTRACT Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at z = 0 using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations project, containing thousands of $$(25\, h^{-1}\, {\rm Mpc})^3$$ volume realizations with varying cosmology, initial random field, stellar and active galactic nucleus (AGN) feedback strength and subgrid model implementation methods. We show that baryonic physics affects matter clustering on scales $$k \gtrsim 0.4\, h\, \mathrm{Mpc}^{-1}$$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive haloes (M200c > 1013.5 M⊙) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of haloes across the full mass range 1010 ≤ Mhalo/M⊙<1015 can predict the effect of galaxy formation on the matter power spectrum on scales k = 1.0–20.0 $$h\, \mathrm{Mpc}^{-1}$$.