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Abstract In the age of large-scale galaxy and lensing surveys, such as DESI, Euclid, Roman, and Rubin, we stand poised to usher in a transformative new phase of data-driven cosmology. To fully harness the capabilities of these surveys, it is critical to constrain the poorly understood influence of baryon feedback physics on the matter power spectrum. We investigate the use of a powerful and novel cosmological probe, fast radio bursts (FRBs), to capture baryonic effects on the matter power spectrum, leveraging simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (or CAMELS) project, including IllustrisTNG, SIMBA, and Astrid. We find that FRB statistics exhibit a strong correlation, independent of the subgrid model and cosmology, with quantities known to encapsulate baryonic impacts on the matter power spectrum, such as baryon spread and the halo baryon fraction. We propose an innovative method utilizing FRB observations to quantify the effects of feedback physics and enhance weak-lensing measurements ofS₈. We outline the necessary steps to prepare for the imminent detection of large FRB populations in the coming years, focusing on understanding the redshift evolution of FRB observables and mitigating the effects of cosmic variance. 

Abstract The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity–stellar mass scaling relation, to investigate how stellar and active galactic nucleus (AGN) feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales (10¹²≲M_halo/M_⊙≲ 10¹³, 10 ≲rkpc⁻¹≲ 400), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All Sky Survey (eRASS) observations, it is found that stronger feedback than is currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match the observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar mass–halo mass relations from observational constraints below the group-mass scale. This tension suggests possible unaccounted-for systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations. 

Abstract The baryonic physics shaping galaxy formation and evolution are complex, spanning a vast range of scales and making them challenging to model. Cosmological simulations rely on subgrid models that produce significantly different predictions. Understanding how models of stellar and active galactic nucleus (AGN) feedback affect baryon behavior across different halo masses and redshifts is essential. Using the SIMBA and IllustrisTNG suites from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we explore the effect of parameters governing the subgrid implementation of stellar and AGN feedback. We find that while IllustrisTNG shows higher cumulative feedback energy across all halos, SIMBA demonstrates a greater spread of baryons, quantified by the closure radius and circumgalactic medium (CGM) gas fraction. This suggests that feedback in SIMBA couples more effectively to baryons and drives them more efficiently within the host halo. There is evidence that the different feedback modes are highly interrelated in these subgrid models. The parameters controlling the stellar feedback efficiency significantly impact AGN feedback, as seen in the suppression of black hole mass growth and delayed activation of AGN feedback to higher-mass halos with increasing stellar feedback efficiency in both simulations. Additionally, the AGN feedback efficiency parameters affect the CGM gas fraction at low halo masses in SIMBA, hinting at complex, nonlinear interactions between the AGN and supernova feedback modes. Overall, we demonstrate that stellar and AGN feedback are intimately interwoven, especially at low redshift, due to subgrid implementation, resulting in halo property effects that might initially seem counterintuitive. 

ABSTRACT Galaxy formation is a complex problem that connects large-scale cosmology with small-scale astrophysics over cosmic time-scales. Hydrodynamical simulations are the most principled approach to model galaxy formation, but have large computational costs. Recently, emulation techniques based on convolutional neural networks (CNNs) have been proposed to predict baryonic properties directly from dark matter simulations. The advantage of these emulators is their ability to capture relevant correlations, but at a fraction of the computational cost compared to simulations. However, training basic CNNs over large redshift ranges is challenging, due to the increasing non-linear interplay between dark matter and baryons paired with the memory inefficiency of CNNs. This work introduces EMBER-2, an improved version of the EMBER (EMulating Baryonic EnRichment) framework, to simultaneously emulate multiple baryon channels including gas density, velocity, temperature, and H i density over a large redshift range, from $z=6$ to $z=0$. EMBER-2 incorporates a context-based styling network paired with Modulated Convolutions for fast, accurate, and memory efficient emulation capable of interpolating the entire redshift range with a single CNN. Although EMBER-2 uses fewer than 1/6 the number of trainable parameters than the previous version, the model improves in every tested summary metric including gas mass conservation and cross-correlation coefficients. The EMBER-2 framework builds the foundation to produce mock catalogues of field level data and derived summary statistics that can directly be incorporated in future analysis pipelines. We release the source code at the official website https://maurbe.github.io/ember2/. 

ABSTRACT The correlations between supermassive black holes (SMBHs) and their host galaxies still defy our understanding from both the observational and theoretical perspectives. Here, we perform pairwise residual analysis on the latest sample of local inactive galaxies with a uniform calibration of their photometric properties and with dynamically measured masses of their central SMBHs. The residuals reveal that stellar velocity dispersion $$\sigma$$ and, possibly host dark matter halo mass $$M_{\rm halo}$$, appear as the galactic properties most correlated with SMBH mass, with a secondary (weaker) correlation with spheroidal (bulge) mass, as also corroborated by additional machine learning tests. These findings may favour energetic/kinetic feedback from active galactic nuclei (AGNs) as the main driver in shaping SMBH scaling relations. Two state-of-the-art hydrodynamic simulations, inclusive of kinetic AGN feedback, are able to broadly capture the mean trends observed in the residuals, although they tend to either favour $$M_{\rm sph}$$ as the most fundamental property, or generate too flat residuals. Increasing AGN feedback kinetic output does not improve the comparison with the data. In the Appendix, we also show that the galaxies with dynamically measured SMBHs are biased high in $$\sigma$$ at fixed luminosity with respect to the full sample of local galaxies, proving that this bias is not a by-product of stellar mass discrepancies. Overall, our results suggest that probing the SMBH–galaxy scaling relations in terms of total stellar mass alone may induce biases, and that either current data sets are incomplete, and/or that more insightful modelling is required to fully reproduce observations. 

Abstract Feedback from supermassive black holes is believed to be a critical driver of the observed color bimodality of galaxies above the Milky Way mass scale. Active galactic nuclei (AGN) feedback has been modeled in many galaxy formation simulations, but most implementations have involved simplified prescriptions or a coarse-grained interstellar medium (ISM). We present the first set of Feedback In Realistic Environments (FIRE)-3 cosmological zoom-in simulations with AGN feedback evolved toz∼ 0, examining the impact of AGN feedback on a set of galaxies with halos in the mass range 10¹²–10¹³M_⊙. These simulations combine detailed stellar and ISM physics with multichannel AGN feedback including radiative feedback, mechanical outflows, and, in some simulations, cosmic rays (CRs). We find that massive (>L*) galaxies in these simulations can match local scaling relations including the stellar mass–halo mass relation and theM_BH–σrelation; in the stronger model with CRs, they also match the size–mass relation and the Faber–Jackson relation. Many of the massive galaxies in the simulations with AGN feedback have quenched star formation and elliptical morphologies, in qualitative agreement with observations. In contrast, simulations at the massive end without AGN feedback produce galaxies that are too massive and form stars too rapidly, are order-of-magnitude too compact, and have velocity dispersions well above Faber–Jackson. Despite these successes, the AGN models analyzed do not produce uniformly realistic galaxies when the feedback parameters are held constant: While the stronger model produces the most realistic massive galaxies, it tends to overquench the lower-mass galaxies. This indicates that further refinements of the AGN modeling are needed. 

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 Detecting dual active galactic nuclei (DAGNs) in observations and understanding theoretically which massive black holes (MBHs) compose them and in which galactic and large-scale environment they reside are becoming increasingly important questions as we enter the multimessenger era of MBH astronomy. This paper presents the abundance and properties of DAGN produced in nine large-scale cosmological hydrodynamical simulations. We focus on DAGN powered by AGN with $$L_{\rm bol}\geqslant 10^{43}\, \rm erg\, s^{-1}$$ and belonging to distinct galaxies, i.e. pairs that can be characterized with current and near-future electromagnetic observations. We find that the number density of DAGN separated by a few to 30 proper kpc varies from $$10^{-8}$$ (or none) to $$10^{-3} \, \rm comoving\, Mpc^{3}$$ in the redshift range $$z=0\!-\!7$$. At a given redshift, the densities of the DAGN numbers vary by up to two orders of magnitude from one simulation to another. However, for all simulations, the DAGN peak is in the range $$z=1\!-\!3$$, right before the peak of cosmic star formation or cosmic AGN activity. The corresponding fractions of DAGN (with respect to the total number of AGN) range from 0 per cent to 6 per cent. We find that simulations could produce too few DAGN at $z=0$ (or merge pairs too quickly) compared to current observational constraints while being consistent with preliminary constraints at high redshift ($$z\sim 3$$). Next-generation observatories (e.g. Advanced X-Ray Imaging Satellite [AXIS]) will be of paramount importance to detect DAGN across cosmic times. We predict the detectability of DAGN with future X-ray telescopes and discuss DAGN as progenitors for future Laser Interferometer Space Antenna (LISA) gravitational wave detections. 

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.