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The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the CAMELS simulation 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 AGN feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales ($10^{12} \lesssim M_{\rm halo}/M_\odot \lesssim 10^{13}$, $10\lesssim r/{\rm kpc} \lesssim 400$), 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 observations, it was found that stronger feedback than currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match 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 systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations., Comment: 13 pages, 6 figures, submitted to ApJ

We report integral field spectroscopy observations with the Near-Infrared Spectrograph on board JWST targeting the 60 kpc environment surrounding the most luminous quasar known at $z=4.6$. We detect ionized gas filaments on 40 kpc scales connecting a network of merging galaxies likely to form a cluster. We find regions of low ionization consistent with large-scale shock excitation surrounding the central dust-obscured quasar, out to distances nearly eight times the effective stellar radius of the quasar host galaxy. In the nuclear region, we find an ionized outflow driven by the quasar with velocities reaching 13,000 km s$^{-1}$, one of the fastest discovered to date with an outflow rate of 2000 M$_\odot$ yr$^{-1}$ and a kinetic luminosity of 6$\times10^{46}$ erg s$^{-1}$ resulting in coupling efficiency between the bolometric luminosity of the quasar and the outflow of 5%. The kinetic luminosity of the outflow is sufficient to power the turbulent motion of the gas on galactic and circumgalactic scales and is likely the primary driver of the radiative shocks on interstellar medium and circumgalactic medium scales. This provides compelling evidence supporting long-standing theoretical predictions that powerful quasar outflows are a main driver in regulating the heating and accretion rate of gas onto massive central cluster galaxies., Comment: 16 pages, 9 figures, 1 table, submitted to ApJ

Detecting dual active galactic nuclei (DAGN) 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 multi-messenger 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 Lbol > 1e43 erg/s and belonging to distinct galaxies, i.e. pairs that can be characterised 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 1e-8 (or none) to 1e-3 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 to 6 percent. 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 = 3). Next-generation observatories (e.g., 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 LISA gravitational wave detections., Comment: Accepted for publication in MNRAS

We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ultraviolet--near infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations from the CAMELS suite, including the Swift-EAGLE, Illustris-TNG, Simba & Astrid galaxy formation models. For each galaxy we calculate the rest-frame luminosity in a number of photometric bands, including the SDSS $\textit{ugriz}$ and GALEX FUV & NUV filters; this dataset represents the largest catalogue of synthetic photometry based on hydrodynamic galaxy formation simulations produced to date, totalling >200 million sources. From these we compile luminosity functions and colour distributions, and find clear dependencies on both cosmology and feedback. We then perform simulation based (likelihood-free) inference using these distributions, and obtain constraints on both cosmological and astrophysical parameters. Both colour distributions and luminosity functions provide complementary information on certain parameters when performing inference. Most interestingly we achieve constraints on $\sigma_8$, describing the clustering of matter. This is attributable to the fact that the photometry encodes the star formation--metal enrichment history of each galaxy; galaxies in a universe with a higher $\sigma_8$ tend to form earlier and have higher metallicities, which leads to redder colours. We find that a model trained on one galaxy formation simulation generalises poorly when applied to another, and attribute this to differences in the subgrid prescriptions, and lack of flexibility in our emission modelling. The photometric catalogues are publicly available at: https://camels.readthedocs.io/ ., Comment: 28 pages, 20 figures, submitted to MNRAS. Comments and feedback welcome!

Coupling black hole (BH) feeding and feedback involves interactions across vast spatial and temporal scales that is computationally challenging. Tracking gas inflows and outflows from kilo-parsec scales to the event horizon for non-spinning BHs in the presence of strong magnetic fields, Cho et al. (2023, 2024) report strong suppression of accretion on horizon scales and low (2%) feedback efficiency. In this letter, we explore the impact of these findings for the supermassive BHs M87* and Sgr A*, using high-resolution, non-cosmological, magnetohydrodynamic (MHD) simulations with the Feedback In Realistic Environments (FIRE-2) model. With no feedback, we find rapid BH growth due to "cooling flows," and for 2% efficiency feedback, while accretion is suppressed, the rates still remain higher than constraints from Event Horizon Telescope (EHT) data (Event Horizon Telescope Collaboration et al. 2021, 2022) for M87* and Sgr A*. To match EHT observations of M87*, a feedback efficiency greater than 15% is required, suggesting the need to include enhanced feedback from BH spin. Similarly, a feedback efficiency of $>15\%$ is needed for Sgr A* to match the estimated observed star formation rate of $\lesssim 2 {\rm M_\odot}$ yr$^{-1}$. However, even with 100% feedback efficiency, the accretion rate onto Sgr A* matches with EHT data only on rare occasions in the simulations, suggesting that Sgr A* is likely in a temporary quiescent phase currently. Bridging accretion and feedback across scales, we conclude that higher feedback efficiency, possibly due to non-zero BH spin, is necessary to suppress "cooling flows" and match observed accretion and star formation rates in M87* and Sgr A*., Comment: 13 pages, 4 figures

The radial distribution of gas within galactic haloes is connected to the star formation rate and the nature of baryon-driven feedback processes. Using six variants of the hydrodynamic simulation Simba, we study the impact of different stellar/AGN feedback prescriptions on the gas density profiles of haloes in the total mass range $10^{11} \, \mathrm{M}_{\odot} < M_{\mathrm{200c}} < 10^{14} \, \mathrm{M}_{\odot}$ and redshift interval $0

The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from Halo Occupation Distribution (HOD) to empirical and semi-analytic models to hydrodynamic. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-Warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar-halo mass relations, concentration-mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong lensing studies., Comment: Submitted to ApJ; 30 + 6 pages; 11 + 4 figures; Comments welcomed

Dwarf galaxies have historically posed challenges to the cold dark matter (CDM) model and, while many of the so-called 'dwarf galaxy problems' have been mitigated by incorporating baryonic processes, the observed diversity of dwarf galaxy rotation curves remains a contentious topic. Meanwhile, the growing observational samples of active galactic nuclei (AGN) in dwarf galaxies have prompted a paradigm shift in our understanding of dwarf galaxy evolution, traditionally thought to be regulated by stellar feedback. In this study, we explore the potential role of AGN feedback in shaping dark matter distributions and increasing the diversity of dwarf galaxy rotation curves, using a new suite of cosmological zoom-in simulations of dwarf galaxies with the FIRE-3 model. Our findings indicate that the presence of active black holes (BHs) in dwarf galaxies can lead to diverse outcomes, ranging from cuspier to more core-like profiles. This variability arises from the dual role of BHs in providing additional feedback and regulating the extent of stellar feedback. Consistent with previous research, we find that AGN feedback is most impactful when cosmic ray (CR) modelling is included, with CRs from any source significantly influencing dark matter profiles. Overall, our results highlight that the interplay between stellar feedback, BHs, and CRs produces a broad spectrum of dark matter density profiles, which align with observed correlations between rotation curve shapes and baryonic dominance. This underscores the importance of including the full range of baryonic processes in dwarf galaxy simulations to address the persistent 'small-scale challenges' to the CDM paradigm., Comment: 22 pages, 9 figures. Submitted to MNRAS. Comments welcome

Elucidating the processes that shape the circumgalactic medium (CGM) is crucial for understanding galaxy evolution. Absorption and emission diagnostics can be interpreted using photoionization calculations to obtain information about the phase and ionization structure of the CGM. For simplicity, typically only the metagalactic background is considered in photoionization calculations, and local sources are ignored. To test this simplification, we perform Monte Carlo radiation transfer on 12 cosmological zoom-in simulations from the Feedback in Realistic Environments (FIRE) project with halo masses $10^{10.5}-10^{13} \mathrm{M}_{\odot}$ in the redshift range $z = 0-3.5$ to determine the spatial extent over which local sources appreciably contribute to the ionizing radiation field in the CGM. We find that on average, the contribution of stars within the galaxy is small beyond one-tenth of the virial radius, $R_{\mathrm{vir}}$, for $z < 1$. For $1 10^{12} \mathrm{M}_{\odot}$ at $z \sim 1.5-2.5$ and for all $M_{\mathrm{vir}}$ considered at $z>3$ , this transition radius can sometimes exceed 0.5 $R_{\mathrm{vir}}$. We also compute the escape fraction at $R_{\mathrm{vir}}$, finding typical values of less than $0.1$, except in higher-mass halos ($M_{\mathrm{halo}} \gtrsim 10^{12} \mathrm{M}_{\odot}$), which have consistently high values of $\sim 0.5-0.6$. Our results indicate that at low redshift, it is reasonable to ignore the ionizing radiation from host-galaxy stars outside of 0.2 $R_{\mathrm{vir}}$, while at Cosmic Noon, local stellar ionizing radiation likely extends further into the CGM and thus should be included in photoionization calculations., Comment: Submitted to OJA

We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over dark matter physics, astrophysics, and cosmology in modeling a range of systems -- from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of Warm Dark Matter, each comprised of 1024 simulations generated using the Arepo code. One suite consists of uniform-box simulations covering a $(25~h^{-1}~{\rm M}_\odot)^3$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the Warm Dark Matter particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and Convolutional Neural Networks, to demonstrate how such simulation suites can be used to disentangle the effects of dark matter and baryonic physics on galactic properties. The DREAMS project can be extended further to include different dark matter models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of dark matter on these scales., Comment: 28 pages, 8 figures, DREAMS website: https://www.dreams-project.org

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial 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, particularly 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 (CAMELS) to include a set of 768 zoom-in simulations of halos in the mass range of $10^{13} - 10^{14.5} M_\odot\,h^{-1}$ that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton $Y-M$, 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 the $Y-M$ relation 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., Comment: The manuscript was submitted to arxiv after receiving and responding to comments from the first referee report

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 like-for-like comparison between 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 $z\approx0$, they achieve this result for markedly different reasons. In EAGLE and SIMBA, we demonstrate that at low halo masses ($M_{\rm 200c}\lesssim 10^{11.5}\, M_{\odot}$), 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 ISM, recycle within the CGM (within $R_{\rm 200c}$). We find that AGN-driven outflows in SIMBA are notably potent, reaching several times $R_{\rm 200c}$ even at halo masses up to $M_{\rm 200c}\approx10^{13.5}\, M_{\odot}$. 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 sub-grid feedback models in the next generation of simulations., Comment: 20 pages, 8 figures. Accepted for publication in MNRAS 03/07/24

We investigate the formation of dense stellar clumps in a suite of high-resolution cosmological zoom-in simulations of a massive, star forming galaxy at $z \sim 2$ under the presence of strong quasar winds. Our simulations include multi-phase ISM physics from the Feedback In Realistic Environments (FIRE) project and a novel implementation of hyper-refined accretion disk winds. We show that powerful quasar winds can have a global negative impact on galaxy growth while in the strongest cases triggering the formation of an off-center clump with stellar mass ${\rm M}_{\star}\sim 10^{7}\,{\rm M}_{\odot}$, effective radius ${\rm R}_{\rm 1/2\,\rm Clump}\sim 20\,{\rm pc}$, and surface density $\Sigma_{\star} \sim 10^{4}\,{\rm M}_{\odot}\,{\rm pc}^{-2}$. The clump progenitor gas cloud is originally not star-forming, but strong ram pressure gradients driven by the quasar winds (orders of magnitude stronger than experienced in the absence of winds) lead to rapid compression and subsequent conversion of gas into stars at densities much higher than the average density of star-forming gas. The AGN-triggered star-forming clump reaches ${\rm SFR} \sim 50\,{\rm M}_{\odot}\,{\rm yr}^{-1}$ and $\Sigma_{\rm SFR} \sim 10^{4}\,{\rm M}_{\odot}\,{\rm yr}^{-1}\,{\rm kpc}^{-2}$, converting most of the progenitor gas cloud into stars in $\sim$2\,Myr, significantly faster than its initial free-fall time and with stellar feedback unable to stop star formation. In contrast, the same gas cloud in the absence of quasar winds forms stars over a much longer period of time ($\sim$35\,Myr), at lower densities, and losing spatial coherency. The presence of young, ultra-dense, gravitationally bound stellar clumps in recently quenched galaxies could thus indicate local positive feedback acting alongside the strong negative impact of powerful quasar winds, providing a plausible formation scenario for globular clusters., Comment: 14 pages, 12 figures. Accepted for publication in MNRAS; doi:10.1093/mnras/stae1021

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. 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 FIRE-3 cosmological zoom-in simulations with AGN feedback evolved to $z\sim0$, examining the impact of AGN feedback on a set of galaxies with halos in the mass range $10^{12}-10^{13} M_{\odot}$. These simulations combine detailed stellar and ISM physics with multi-channel 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 the $M_{\rm BH}$-$\sigma$ relation; 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 over-quench the lower-mass galaxies. This indicates that further refinements of the AGN modeling are needed., Comment: 18 pages, 8 figures, accepted by ApJ

In a companion paper, we reported the self-consistent formation of quasar accretion disks with inflow rates $\sim 10\,{\rm M_{\odot}\,yr^{-1}}$ down to <300 Schwarzschild radii from cosmological radiation-magneto-thermochemical-hydrodynamical galaxy and star formation simulations. We see the formation of a well-defined, steady-state accretion disk which is stable against star formation at sub-pc scales. The disks are optically thick, with radiative cooling balancing accretion, but with properties that are distinct from those assumed in most previous accretion disk models. The pressure is strongly dominated by (primarily toroidal) magnetic fields, with a plasma $\beta \sim 10^{-4}$ even in the disk midplane. They are qualitatively distinct from magnetically elevated or arrested disks. The disks are strongly turbulent, with trans-Alfvenic and highly super-sonic turbulence, and balance this via a cooling time that is short compared to the disk dynamical time, and can sustain highly super-Eddington accretion rates. Their surface and 3D densities at $\sim 10^{3}-10^{5}$ gravitational radii are much lower than in a Shakura-Sunyaev disk, with important implications for their thermo-chemistry and stability. We show how the magnetic field strengths and geometries arise from rapid advection of flux with the inflow from much weaker galaxy-scale fields in these 'flux-frozen' disks, and how this stabilizes the disk and gives rise to efficient torques. Re-simulating without magnetic fields produces catastrophic fragmentation with a vastly smaller, lower-$\dot{M}$ Shakura-Sunyaev-like disk., Comment: 46 pages, 30 figures. Updated to match accepted version from The Open Journal of Astrophysics. Part of a series with arXiv:2309.13115 . Animations of the simulations can be viewed at http://www.tapir.caltech.edu/~phopkins/Site/animations/Movies_zoom.html

It has recently become possible to zoom-in from cosmological to sub-pc scales in galaxy simulations to follow accretion onto supermassive black holes (SMBHs). However, at some point the approximations used on ISM scales (e.g. optically-thin cooling and stellar-population-integrated star formation [SF] and feedback [FB]) break down. We therefore present the first cosmological radiation-magnetohydrodynamic (RMHD) simulation which self-consistently combines the FIRE physics (relevant on galactic/ISM scales where SF/FB are ensemble-averaged) and STARFORGE physics (relevant on small scales where we track individual (proto)stellar formation and evolution), together with explicit RMHD (including non-ideal MHD and multi-band M1-RHD) which self-consistently treats both optically-thick and thin regimes. This allows us to span scales from ~100 Mpc down to <100 au (~300 Schwarzschild radii) around a SMBH at a time where it accretes as a bright quasar, in a single simulation. We show that accretion rates up to $\sim 10-100\,{\rm M_{\odot}\,yr^{-1}}$ can be sustained into the accretion disk at $\ll 10^{3}\,R_{\rm schw}$, with gravitational torques between stars and gas dominating on sub-kpc scales until star formation is shut down on sub-pc scales by a combination of optical depth to cooling and strong magnetic fields. There is an intermediate-scale, flux-frozen disk which is gravitoturbulent and stabilized by magnetic pressure sustaining strong turbulence and inflow with persistent spiral modes. In this paper we focus on how gas gets into the small-scale disk, and how star formation is efficiently suppressed., Comment: 38 pages, 18 figures. Replaced with accepted version

Recent works have discovered a relatively tight correlation between $\Omega_{\rm m}$ and properties of individual simulated galaxies. Because of this, it has been shown that constraints on $\Omega_{\rm m}$ can be placed using the properties of individual galaxies while accounting for uncertainties on astrophysical processes such as feedback from supernova 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 ($\Omega_{\rm m}$ and $\sigma_8$) 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 $\Omega_{\rm m}$ inference. 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 $\Omega_{\rm m}$ from 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., Comment: 13 pages, 7 figures

The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets 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 (HI) radio 2D 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'' datasets comprised of both HI 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 halos with masses $\log(M_{\rm{halo}}/M_{\odot}) < 12.5$. 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 physical CGM properties hinges on our ability to interpret these deep-learning models.

We quantify the cosmological spread of baryons relative to their initial neighboring 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 neighboring 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 neighboring 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 \textsc{SIMBA} model spreading $\sim$40\% of baryons $>$1\,Mpc away compared to $\sim$10\% for the IllustrisTNG and \textsc{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 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$^{-1}$ while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation., Comment: 17 pages, 15 figures

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship., Comment: 8 pages, 2 figures, accepted for ICML 2023 Workshop on Machine Learning for Astrophysics

We explore the role of galactic feedback on the low redshift Lyman-$\alpha$ (Ly$\alpha$)~forest ($z \lesssim 2$) 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 sub-grid models. We find that both AGN and stellar feedback in Simba play a role in setting the Ly$\alpha$ forest 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 the $b$-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 Ly$\alpha$ forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-$z$ Ly$\alpha$ forest can be sensitive to changes in the ultraviolet background (UVB), stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM., Comment: 27 pages, 12 figures, 2 tables, accepted to ApJ

We present a new suite of over 1,500 cosmological N-body simulations with varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV at a 95% confidence level from small maps that cover an area of (25 h$^{-1}$ Mpc)$^2$. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h$^{-1}$ kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make 2x more precise predictions and can make accurate predictions to 2x as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment., Comment: 16 pages, 12 figures

Massive black holes are fundamental constituents of our cosmos, from the Big Bang to today. Understanding their formation from cosmic dawn, their growth, and the emergence of the first, rare quasars in the early Universe remains one of our greatest theoretical and observational challenges. Hydrodynamic cosmological simulations self-consistently combine the processes of structure formation at cosmological scales with the physics of smaller, galaxy scales. They capture our most realistic understanding of massive black holes and their connection to galaxy formation and have become the primary avenue for theoretical research in this field. The space-based gravitational wave interferometer, LISA, will open up new investigations into the dynamical processes involving massive black holes. Multi-messenger astrophysics brings new exciting prospects for tracing the origin, growth and merger history of massive black holes across cosmic ages., Comment: 77 pages, 20 figures, This chapter is the preprint of the version currently in production. Please cite this chapter as the following: T.DiMatteo, D. Angles-Alcazar, and F. Shankar. Massive black holes in galactic nuclei: Theory and simulations, in The Encyclopedia of Cosmology (Set 2): Black Holes, edited by Z. Haiman (World Scientific, New Jersey, 2023)

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter $\Omega_{\rm m}$, at fixed $\Omega_{\rm b}$, and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that made use of a distinct subgrid physics: Astrid. We find that also in this case, neural networks are able to infer the value of $\Omega_{\rm m}$ with a $\sim10\%$ precision from the properties of individual galaxies while accounting for astrophysics uncertainties as modeled in CAMELS. This tight relationship is present at all considered redshifts, $z\leq3$, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes., Comment: 16 pages, 12 figures

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 2,124 hydrodynamic simulation runs that vary 3 cosmological parameters ($\Omega_m$, $\sigma_8$, $\Omega_b$) and 4 parameters controlling stellar and 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 non-linear 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.

We discover analytic equations that can infer the value of $\Omega_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $\Omega_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $\Omega_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $\Omega_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $\Omega_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$., Comment: 32 pages, 13 figures, summary video: https://youtu.be/STZHvDHkVgo

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain $3$D positions and radial velocities of $\sim 1, 000$ galaxies in tiny $(25~h^{-1}{\rm Mpc})^3$ volumes our models can infer the value of $\Omega_{\rm m}$ with approximately $12$ % precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models - IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE -, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on $1,024$ simulations that cover a vast region in parameter space - variations in $5$ cosmological and $23$ astrophysical parameters - finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than $\sim10~h^{-1}{\rm kpc}$., Comment: 34 pages, 12 figures. For a video summarizing the results, see https://youtu.be/b59ep7cyPOs

Observations of local star-forming galaxies (SFGs) show a tight correlation between their singly ionized carbon line luminosity ($L_{\rm [C_{II}]}$) and star formation rate (SFR), suggesting that $L_{\rm [C_{II}]}$ may be a useful SFR tracer for galaxies. Some other galaxy populations, however, are found to have lower $L_{\rm [C_{II}]}{}/{}\rm SFR$ than the local SFGs, including the infrared-luminous, starburst galaxies at low and high redshifts, as well as some moderately star-forming galaxies at the epoch of re-ionization (EoR). The origin of this `$\rm [C_{II}]$ deficit' is unclear. In this work, we study the $L_{\rm [C_{II}]}$-SFR relation of galaxies using a sample of $z=0-8$ galaxies with $M_*\approx10^7-5\times10^{11}\,M_\odot$ extracted from cosmological volume and zoom-in simulations from the Feedback in Realistic Environments (FIRE) project. We find a simple analytic expression for $L_{\rm [C_{II}]}$/SFR of galaxies in terms of the following parameters: mass fraction of $\rm [C_{II}]$-emitting gas ($f_{\rm [C_{II}]}$), gas metallicity ($Z_{\rm gas}$), gas density ($n_{\rm gas}$) and gas depletion time ($t_{\rm dep}{}={}M_{\rm gas}{}/{}\rm SFR$). We find two distinct physical regimes, where $t_{\rm dep}$ ($Z_{\rm gas}$) is the main driver of the $\rm [C_{II}]$ deficit in $\rm H_2$-rich ($\rm H_2$-poor) galaxies. The observed $\rm [C_{II}]$ deficit of IR-luminous galaxies and early EoR galaxies, corresponding to the two different regimes, is due to short gas depletion time and low gas metallicity, respectively. Our result indicates that $\rm [C_{II}]$ deficit is a common phenomenon of galaxies, and caution needs to be taken when applying a constant $L_{\rm [C_{II}]}$-to-SFR conversion factor derived from local SFGs to estimate cosmic SFR density at high redshifts and interpret data from upcoming $\rm [C_{II}]$ line intensity mapping experiments., Comment: 44 page, 24 figures, 8 tables and 8 appendices, accepted for publication in MNRAS. Key figures: fig. 16 & 17. Comments are welcome

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 (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid 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 halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) 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 halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$.

Feedback from active galactic nuclei and stellar processes changes the matter distribution on small scales, leading to significant systematic uncertainty in weak lensing constraints on cosmology. We investigate how the observable properties of group-scale halos can constrain feedback's impact on the matter distribution using Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS). Extending the results of previous work to smaller halo masses and higher wavenumber, $k$, we find that the baryon fraction in halos contains significant information about the impact of feedback on the matter power spectrum. We explore how the thermal Sunyaev Zel'dovich (tSZ) signal from group-scale halos contains similar information. Using recent Dark Energy Survey (DES) weak lensing and Atacama Cosmology Telescope (ACT) tSZ cross-correlation measurements and models trained on CAMELS, we obtain $10\%$ constraints on feedback effects on the power spectrum at $k \sim 5\, h/{\rm Mpc}$. We show that with future surveys, it will be possible to constrain baryonic effects on the power spectrum to $\mathcal{O}(<1\%)$ at $k = 1\, h/{\rm Mpc}$ and $\mathcal{O}(3\%)$ at $k = 5\, h/{\rm Mpc}$ using the methods that we introduce here. Finally, we investigate the impact of feedback on the matter bispectrum, finding that tSZ observables are highly informative in this case., Comment: 18 pages, 15 figures. Comments are welcome

Negative feedback from accreting supermassive black holes is regarded as a key ingredient in suppressing star formation and quenching massive galaxies. However, several models and observations suggest that black hole feedback may have a positive effect, triggering star formation by compressing interstellar medium gas to higher densities. We investigate the dual role of black hole feedback using cosmological hydrodynamic simulations from the Feedback In Realistic Environments (FIRE) project, including a novel implementation of hyper-refined accretion-disc winds. Focusing on a massive, star-forming galaxy at $z \sim 2$ ($M_{\rm halo} \sim 10^{12.5} \, {\rm M}_{\odot}$), we show that strong quasar winds with kinetic power $\sim$10$^{46}$ erg/s acting for $>$20$\,$Myr drive the formation of a central gas cavity and can dramatically reduce the star formation rate surface density across the galaxy disc. The suppression of star formation is primarily driven by reducing the amount of gas that can become star-forming, compared to directly evacuating the pre-existing star-forming gas reservoir (preventive feedback dominates over ejective feedback). Despite the global negative impact of quasar winds, we identify several plausible signatures of local positive feedback, including: (1) spatial anti-correlation of wind-dominated regions and star-forming clumps, (2) higher local star formation efficiency in compressed gas near the edge of the cavity, and (3) increased local contribution of outflowing material to star formation. Stars forming under the presence of quasar winds tend to do so at larger radial distances. Our results suggest that positive and negative AGN feedback can coexist in galaxies, but local positive triggering of star formation plays a minor role in global galaxy growth., Comment: 17 pages, 12 figures. Accepted for publication in MNRAS

In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ~1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates to the cosmological simulations. Using the cosmic star formation rate density (SFRD) and, separately, stellar mass functions (SMFs) at different redshifts, we perform ILI on selected cosmological and astrophysical parameters (Omega_m, sigma_8, stellar wind feedback, and kinetic black hole feedback) and obtain full 6-dimensional posterior distributions. In the performance test, the ILI from the emulated SFRD (SMFs) can recover the target observables with a relative error of 0.17% (0.4%). We find that degeneracies exist between the parameters inferred from the emulated SFRD, confirmed with new full cosmological simulations. We also find that the SMFs can break the degeneracy in the SFRD, which indicates that the SMFs provide complementary constraints for the parameters. Further, we find that the parameter combination inferred from an observationally-inferred SFRD reproduces the target observed SFRD very well, whereas, in the case of the SMFs, the inferred and observed SMFs show significant discrepancies that indicate potential limitations of the current galaxy formation modeling and calibration framework, and/or systematic differences and inconsistencies between observations of the stellar mass function., Comment: This is the revised version from the reviewer's report (submitted to ApJ)

The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However, the details of how galaxies and their CGM co-evolve remain poorly understood. We present a new time-dependent two-zone model that self-consistently tracks not just mass and metal flows between galaxies and their CGM but also the evolution of the global thermal and turbulent kinetic energy of the CGM. Our model accounts for heating and turbulence driven by both supernova winds and cosmic accretion as well as radiative cooling, turbulence dissipation, and halo outflows due to CGM overpressurization. We demonstrate that, depending on parameters, the CGM can undergo a phase transition (``thermalization'') from a cool, turbulence-supported phase to a virial-temperature, thermally-supported phase. This CGM phase transition is largely determined by the ability of radiative cooling to balance heating from supernova winds and turbulence dissipation. We perform an initial calibration of our model to the FIRE-2 cosmological hydrodynamical simulations and show that it can approximately reproduce the baryon cycles of the simulated halos. In particular, we find that, for these parameters, the phase transition occurs at high-redshift in ultrafaint progenitors and at low redshift in classical $M_{\rm vir}\sim10^{11}M_{\odot}$ dwarfs, while Milky Way-mass halos undergo the transition at $z\approx0.5$. We see a similar transition in the simulations though it is more gradual, likely reflecting radial dependence and multi-phase gas not captured by our model. We discuss these and other limitations of the model and possible future extensions., Comment: Updated to reflect version accepted by ApJ; minor text edits to clarify limitations and caveats of model

Several recent simulations of galaxy formation predict two main phases of supermassive black hole (BH) accretion: an early, highly intermittent phase (during which BHs are under-massive relative to local scaling relations), followed by a phase of accelerated growth. We investigate physical factors that drive the transition in BH accretion in cosmological zoom-in simulations from the FIRE project, ranging from dwarf galaxies to galaxies sufficiently massive to host luminous quasars. The simulations model multi-channel stellar feedback, but neglect AGN feedback. We show that multiple physical properties, including halo mass, galaxy stellar mass, and depth of the central gravitational potential correlate with accelerated BH fueling: constant thresholds in these properties are typically crossed within ~0.1 Hubble time of accelerated BH fueling. Black hole masses increase sharply when the stellar surface density in the inner 1 kpc crosses a threshold Sigma1 ~ 10^9.5 Msun/kpc^2, a characteristic value above which gravity prevents stellar feedback from ejecting gas, and similar to the value above which galaxies are observed to quench. We further show that accelerated BH growth correlates with the emergence of long-lived, thin gas disks, as well as with virialization of the inner circumgalactic medium. The halo mass Mh ~ 10^12 Msun and stellar mass Mstar ~ 10^10.5 Msun at which BH growth accelerates correspond to ~L* galaxies. The fact that stellar feedback becomes inefficient at ejecting gas from the nucleus above this mass scale may play an important role in explaining why AGN feedback appears to be most important in galaxies above ~L*., Comment: 21 pages, 9 figures, submitted to MNRAS

Active galactic nuclei (AGN) feedback models are generally calibrated to reproduce galaxy observables such as the stellar mass function and the bimodality in galaxy colors. We use variations of the AGN feedback implementations in the IllustrisTNG (TNG) and Simba cosmological hydrodynamic simulations to show that the low redshift Lyman-$\alpha$ forest can provide constraints on the impact of AGN feedback. We show that TNG over-predicts the number density of absorbers at column densities $N_{\rm HI} < 10^{14}$ cm$^{-2}$ compared to data from the Cosmic Origins Spectrograph (in agreement with previous work), and we demonstrate explicitly that its kinetic feedback mode, which is primarily responsible for galaxy quenching, has a negligible impact on the column density distribution (CDD) of absorbers. In contrast, we show that the fiducial Simba model which includes AGN jet feedback is the preferred fit to the observed CDD of the $z = 0.1$ Lyman-$\alpha$ forest across five orders of magnitude in column density. We show that the Simba results with jets produce a quantitatively better fit to the observational data than the Simba results without jets, even when the UVB is left as a free parameter. AGN jets in Simba are high speed, collimated, weakly-interacting with the interstellar medium (via brief hydrodynamic decoupling) and heated to the halo virial temperature. Collectively these properties result in stronger long-range impacts on the IGM when compared to TNG's kinetic feedback mode, which drives isotropic winds with lower velocities at the galactic radius. Our results suggest that the low redshift Lyman-$\alpha$ forest provides plausible evidence for long-range AGN jet feedback., Comment: 15 pages, 5 figures, 1 table, accepted to ApJL

We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $\Omega_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters., Comment: 25 pages, 11 figures, summary video: https://youtu.be/qkw92Z6owJU

Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos ($Y_\mathrm{SZ}$) from CMB surveys, and cause its relation with the halo mass ($Y_\mathrm{SZ}-M$) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study of such deviations using CAMELS, a suite of hydrodynamic simulations with extensive variations in feedback prescriptions. We use a combination of two machine learning tools (random forest and symbolic regression) to search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses ($M\lesssim 10^{14}\, h^{-1} \, M_\odot$); we find that simply replacing $Y\rightarrow Y(1+M_*/M_\mathrm{gas})$ in the relation makes it remarkably self-similar. This could serve as a robust multiwavelength mass proxy for low-mass clusters and galaxy groups. Our methodology can also be generally useful to improve the domain of validity of other astrophysical scaling relations. We also forecast that measurements of the $Y-M$ relation could provide percent-level constraints on certain combinations of feedback parameters and/or rule out a major part of the parameter space of supernova and AGN feedback models used in current state-of-the-art hydrodynamic simulations. Our results can be useful for using upcoming SZ surveys (e.g., SO, CMB-S4) and galaxy surveys (e.g., DESI and Rubin) to constrain the nature of baryonic feedback. Finally, we find that the an alternative relation, $Y-M_*$, provides complementary information on feedback than $Y-M$, Comment: Version appearing in MNRAS. Minor change to Fig.6 and added Fig. A5 compared to the previous version. 7+5 pages. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io., Comment: 28 pages main text, additional 12 in appendices, 23 figures, 9 tables

Feedback from accreting supermassive black holes (SMBHs) is thought to be a primary driver of quenching in massive galaxies, but the best way to implement SMBH physics into galaxy formation simulations remains ambiguous. As part of the Feedback in Realistic Environments (FIRE) project, we explore the effects of different modeling choices for SMBH accretion and feedback in a suite of $\sim500$ cosmological zoom-in simulations across a wide range of halo mass (10^10-10^13 Msun). Within the suite, we vary the numerical schemes for BH accretion and feedback, the accretion efficiency, and the strength of mechanical, radiative, and cosmic ray feedback independently. We then compare the outcomes to observed galaxy scaling relations. We find several models that satisfy the observational constraints, and for which the energetics in different feedback channels are physically plausible. Interestingly, cosmic rays accelerated by SMBHs play an important role in many successful models. However, it is non-trivial to reproduce scaling relations across halo mass, and many model variations produce qualitatively incorrect results regardless of parameter choices. The growth of stellar and BH mass are closely related: for example, over-massive BHs tend to over-quench galaxies. BH mass is most strongly affected by the choice of accretion efficiency in high-mass halos, but by feedback efficiency in low-mass halos. The amount of star formation suppression by SMBH feedback in low-mass halos is determined primarily by the time-integrated feedback energy. For massive galaxies, the "responsiveness" of a model (i.e. how quickly and powerfully the BH responds to gas available for accretion) is an additional important factor for quenching., Comment: 18 pages, 9 figures. Submitted to MNRAS, comments welcome

Increasingly, uncertainties in predictions from galaxy formation simulations (at sub-Milky Way masses) are dominated by uncertainties in stellar evolution inputs. In this paper, we present the full set of updates from the FIRE-2 version of the Feedback In Realistic Environments (FIRE) project code, to the next version, FIRE-3. While the transition from FIRE-1 to FIRE-2 focused on improving numerical methods, here we update the stellar evolution tracks used to determine stellar feedback inputs, e.g. stellar mass-loss (O/B and AGB), spectra (luminosities and ionization rates), and supernova rates (core-collapse and Ia), as well as detailed mass-dependent yields. We also update the low-temperature cooling and chemistry, to enable improved accuracy at $T \lesssim 10^{4}\,$K and densities $n\gg 1\,{\rm cm^{-3}}$, and the meta-galactic ionizing background. All of these synthesize newer empirical constraints on these quantities and updated stellar evolution and yield models from a number of groups, addressing different aspects of stellar evolution. To make the updated models as accessible as possible, we provide fitting functions for all of the relevant updated tracks, yields, etc, in a form specifically designed so they can be directly 'plugged in' to existing galaxy formation simulations. We also summarize the default FIRE-3 implementations of 'optional' physics, including spectrally-resolved cosmic rays and supermassive black hole growth and feedback., Comment: 26 pages, 12 figures. Submitted to MNRAS. Comments welcome

We describe a public data release of the FIRE-2 cosmological zoom-in simulations of galaxy formation, available at http://flathub.flatironinstitute.org/fire, from the Feedback In Realistic Environments (FIRE) project. FIRE-2 simulations achieve parsec-scale resolution to explicitly model the multi-phase interstellar medium while implementing direct models for stellar evolution and feedback, including stellar winds, core-collapse and Ia supernovae, radiation pressure, photoionization, and photoelectric heating. We release complete snapshots from 3 suites of simulations. The first comprises 20 simulations that zoom in on 14 Milky Way-mass galaxies, 5 SMC/LMC-mass galaxies, and 4 lower-mass galaxies including 1 ultra-faint; we release 39 snapshots across z = 0 - 10. The second comprises 4 massive galaxies, with 19 snapshots across z = 1 - 10. Finally, a high-redshift suite comprises 22 simulations, with 11 snapshots across z = 5 - 10. Each simulation also includes dozens of resolved lower-mass (satellite) galaxies in its zoom-in region. Snapshots include all stored properties for all dark matter, gas, and star particles, including 11 elemental abundances for stars and gas, and formation times (ages) of star particles. We also release accompanying (sub)halo catalogs, which include galaxy properties and member star particles. For the simulations to z = 0, including all Milky Way-mass galaxies, we release the formation coordinates and an "ex-situ" flag for all star particles, pointers to track particles across snapshots, catalogs of stellar streams, and multipole basis expansions for the halo mass distributions. We describe publicly available python packages for reading and analyzing these simulations., Comment: 22 pages. Accepted for publication in ApJS, matches the published version, with updated references. Now includes up to 39 snapshots per simulation and formation coordinates for all star particles. Data available at http://flathub.flatironinstitute.org/fire

The standard cold dark matter plus cosmological constant model predicts that galaxies form within dark-matter haloes, and that low-mass galaxies are more dark-matter dominated than massive ones. The unexpected discovery of two low-mass galaxies lacking dark matter immediately provoked concerns about the standard cosmology and ignited explorations of alternatives, including self-interacting dark matter and modified gravity. Apprehension grew after several cosmological simulations using the conventional model failed to form adequate numerical analogues with comparable internal characteristics (stellar masses, sizes, velocity dispersions and morphologies). Here we show that the standard paradigm naturally produces galaxies lacking dark matter with internal characteristics in agreement with observations. Using a state-of-the-art cosmological simulation and a meticulous galaxy-identification technique, we find that extreme close encounters with massive neighbours can be responsible for this. We predict that approximately 30 percent of massive central galaxies (with at least 1e11 solar masses in stars) harbour at least one dark-matter-deficient satellite (with 1e8 - 1e9 solar masses in stars). This distinctive class of galaxies provides an additional layer in our understanding of the role of interactions in shaping galactic properties. Future observations surveying galaxies in the aforementioned regime will provide a crucial test of this scenario., Comment: 55 pages, 4 figures, 13 supplementary figures. 29 pages, 4 figures. Accepted for publication in Nature Astronomy. To appear on 14-February-2022. Published version: https://www.nature.com/articles/s41550-021-01598-4

The James Webb Space Telescope will have the power to characterize high-redshift quasars at z>6 with an unprecedented depth and spatial resolution. While the brightest quasars at such redshift (i.e., with bolometric luminosity L_bol> 10^46 erg/s) provide us with key information on the most extreme objects in the Universe, measuring the black hole (BH) mass and Eddington ratios of fainter quasars with L_bol= 10^45-10^46 erg/s opens a path to understand the build-up of more normal BHs at z>6. In this paper, we show that the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA large-scale cosmological simulations do not agree on whether BHs at z>4 are overmassive or undermassive at fixed galaxy stellar mass with respect to the M_BH-M_star scaling relation at z=0 (BH mass offsets). Our conclusions are unchanged when using the local scaling relation produced by each simulation or empirical relations. We find that the BH mass offsets of the simulated faint quasar population at z>4, unlike those of bright quasars, represent the BH mass offsets of the entire BH population, for all the simulations. Thus, a population of faint quasars with L_bol= 10^45-10^46 erg/s observed by JWST can provide key constraints on the assembly of BHs at high redshift. Moreover, this will help constraining the high-redshift regime of cosmological simulations, including BH seeding, early growth, and co-evolution with the host galaxies. Our results also motivate the need for simulations of larger cosmological volumes down to z=6, with the same diversity of sub-grid physics, in order to gain statistics on the most extreme objects at high redshift., Comment: published in MNRAS, 19 pages, 8 figures, key figures: Fig. 3, Fig.5, and Fig. 7

Recent systematic searches for massive black holes (BHs) in local dwarf galaxies led to the discovery of a population of faint Active Galactic Nuclei (AGN). We investigate the agreement of the BH and AGN populations in the Illustris, TNG, Horizon-AGN, EAGLE, and SIMBA simulations with current observational constraints in low-mass galaxies. We find that some of these simulations produce BHs that are too massive, and that the BH occupation fraction at z=0 is not inherited from the simulation seeding modeling. The ability of BHs and their host galaxies to power an AGN depends on BH and galaxy subgrid modeling. The fraction of AGN in low-mass galaxies is not used to calibrate the simulations, and thus can be used to differentiate galaxy formation models. AGN fractions at z=0 span two orders of magnitude at fixed galaxy stellar mass in simulations, similarly to observational constraints, but uncertainties and degeneracies affect both observations and simulations. The agreement is difficult to interpret due to differences in the masses of simulated and observed BHs, BH occupation fraction affected by numerical choices, and an unknown fraction of obscured AGN. Our work advocates for more thorough comparisons with observations to improve the modeling of cosmological simulations, and our understanding of BH and galaxy physics in the low-mass regime. The mass of BHs, their ability to efficiently accrete gas, and the AGN fraction in low-mass galaxies have important implications for the build-up of the entire BH and galaxy populations with time., Comment: Accepted in MNRAS, 21 pages, 11 figures, 1 table

We use FIRE simulations to study disk formation in z~0, Milky Way-mass galaxies, and conclude that a key ingredient for the formation of thin stellar disks is the ability for accreting gas to develop an aligned angular momentum distribution via internal cancellation *prior* to joining the galaxy. Among galaxies with a high fraction (>70%) of their young stars in a thin disk (h/R~0.1) we find that: (i) hot, virial-temperature gas dominates the inflowing gas mass on halo scales (>~20 kpc), with radiative losses offset by compression heating; (ii) this hot accretion proceeds until angular momentum support slows inward motion, at which point the gas cools to T~10^4 K or less; (iii) prior to cooling, the accreting gas develops an angular momentum distribution that is aligned with the galaxy disk, and while cooling transitions from a quasi-spherical spatial configuration to a more flattened, disk-like configuration. We show that the existence of this "rotating cooling flow" accretion mode is strongly correlated with the fraction of stars forming in a thin disk among a sample of 17 z~0 galaxies spanning a halo mass range of 10^10.5 solar masses to 10^12 solar masses, or a stellar mass range 10^8 solar masses to 10^11 solar masses. Notably, galaxies with a thick disk or irregular morphology do not undergo significant angular momentum alignment of gas prior to accretion and show no correspondence between halo gas cooling and flattening. Our results suggest that rotating cooling flows (or, more generally, rotating subsonic flows) that become coherent and angular momentum-supported prior to accretion onto the galaxy are likely a necessary condition for the formation of thin, star-forming disk galaxies in a LambdaCDM universe., Comment: 17 pages, 11 figures. Comments welcome. Accompanying interactive visualization at zhafen.github.io/rotating-cooling-flows

Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback. We use a suite of (magneto-)hydrodynamic simulations from the CAMELS project and perform an idealized analysis to forecast statistical uncertainties on a limited set of cosmological and physically-motivated astrophysical parameters. We find that the electron number density auto-correlation, measurable through either kinematic Sunyaev-Zel'dovich observations or through Fast Radio Burst dispersion measures, provides tight constraints on $\Omega_{m}$ and the mean baryon fraction in intermediate-mass halos, $\bar{f}_{\mathrm{bar}}$. By obtaining an empirical measure for the associated systematic uncertainties, we find these constraints to be largely robust to differences in baryonic feedback models implemented in hydrodynamic simulations. We further discuss the main caveats associated with our analysis, and point out possible directions for future work., Comment: 31 pages, 10 figures, to be submitted to JCAP

The cycle of baryons through the circumgalactic medium (CGM) is important to understand 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), that varies four different feedback parameters of two previously existing hydrodynamical simulations, IllustrisTNG and SIMBA. We capture the dependencies 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 DESI-like (Dark Energy Spectroscopic Instrument) galaxy sample observed by the Simons Observatory all four feedback parameters are able to be constrained (some within the $10\%$ level), indicating that future observations will be able to further restrict the parameter space for these sub-grid 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 AGN feedback parameter variation in the CAMELS simulation suite, we cannot reproduce the tSZ signal of galaxies selected by the Baryon Oscillation Spectroscopic Survey as measured by the Atacama Cosmology Telescope., Comment: 23 pages, 7 figures, comments welcome

Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of $\Omega_{\rm m}$, at fixed $\Omega_{\rm b}$, with a $\sim10\%$ precision, while no constraint can be placed on $\sigma_8$. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, $z\leq3$, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of $\Omega_{\rm m}$. We believe that our results can be explained taking into account that changes in the value of $\Omega_{\rm m}$, or potentially $\Omega_{\rm b}/\Omega_{\rm m}$, affect the dark matter content of galaxies. That effect leaves a distinct signature in galaxy properties to the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics., Comment: 20+6 pages, 15 figures, data and codes to reproduce the results publicly available at https://github.com/franciscovillaescusa/Cosmo1gal

High-significance measurements of the monopole thermal Sunyaev-Zel'dovich CMB spectral distortions have the potential to tightly constrain poorly understood baryonic feedback processes. The sky-averaged Compton-y distortion and its relativistic correction are measures of the total thermal energy in electrons in the observable universe and their mean temperature. We use the CAMELS suite of hydrodynamic simulations to explore possible constraints on parameters describing the subgrid implementation of feedback from active galactic nuclei and supernovae, assuming a PIXIE-like measurement. The small 25 Mpc/h CAMELS boxes present challenges due to the significant cosmic variance. We utilize machine learning to construct interpolators through the noisy simulation data. Using the halo model, we translate the simulation halo mass functions into correction factors to reduce cosmic variance where required. Our results depend on the subgrid model. In the case of IllustrisTNG, we find that the best-determined parameter combination can be measured to ~2% and corresponds to a product of AGN and SN feedback. In the case of SIMBA, the tightest constraint is ~0.2% on a ratio between AGN and SN feedback. A second orthogonal parameter combination can be measured to ~8%. Our results demonstrate the significant constraining power a measurement of the late-time spectral distortion monopoles would have for baryonic feedback models., Comment: 10+5 pages, 7 figures; matches PRD published version

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_\mathrm{SZ}-M$), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$. $Y_\mathrm{conc}$ reduces the scatter in the predicted $M$ by $\sim 20-30$\% for large clusters ($M\gtrsim 10^{14}\, h^{-1} \, M_\odot$), as compared to using just $Y_\mathrm{SZ}$. We show that the dependence on $c_\mathrm{gas}$ is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test $Y_\mathrm{conc}$ on clusters from CAMELS simulations and show that $Y_\mathrm{conc}$ is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4., Comment: Version appearing in PNAS. Added additional tests but results unchanged compared to previous version. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML