Torque-Limited Growth of Massive Black Holes in Galaxies Across Cosmic Time

We combine cosmological hydrodynamic simulations with analytic models to evaluate the role of galaxy-scale gravitational torques on the evolution of massive black holes at the centers of star-forming galaxies. We confirm and extend our earlier results to show that torque-limited growth yields black holes and host galaxies evolving on average along the Mbh-Mbulge relation from early times down to z = 0 and that convergence onto the scaling relation occurs independent of the initial conditions and with no need for mass averaging through mergers or additional self-regulation processes. Smooth accretion dominates the long-term evolution, with black hole mergers with mass ratios >1:5 representing typically a small fraction of the total growth. Winds from the accretion disk are required to eject significant mass to suppress black hole growth, but there is no need for coupling this wind to galactic-scale gas to regulate black holes in a non-linear feedback loop. Torque-limited growth yields a close-to-linear relation for the star formation rate and the black hole accretion rate averaged over galaxy evolution time scales. However, the SFR-AGN connection has significant scatter owing to strong variability of black hole accretion at all resolved time scales. Eddington ratios can be described by a broad lognormal distribution with median value evolving roughly as (1 + z)^1.9, suggesting a main sequence for black hole growth similar to the cosmic evolution of specific SFRs. Our results offer an attractive scenario consistent with available observations in which cosmological gas infall and transport of angular momentum in the galaxy by gravitational instabilities regulate the long-term co-evolution of black holes and star-forming galaxies.Comment: 26 pages, 15 figures, replaced by published versio

Disappearing galaxies: the orientation dependence of JWST-bright, HST-dark, star-forming galaxy selection

Galaxies that are invisible in deep optical-NIR imaging but detected at longer wavelengths have been the focus of several recent observational studies, with speculation that they could constitute a substantial missing population and even dominate the cosmic star formation rate density at $z\gtrsim4$. The depths now achievable with JWST at the longest wavelengths probed by HST, coupled with the transformative resolution at longer wavelengths, are already enabling detailed, spatially-resolved characterisation of sources that were invisible to HST, often known as `HST-dark' galaxies. However, until now, there has been little theoretical work to compare against. We present the first simulation-based study of this population, using highly-resolved galaxies from the Feedback in Realistic Environments (FIRE) project, with multi-wavelength images along several lines of sight forward-modelled using radiative transfer. We naturally recover a population of modelled sources that meet commonly-used selection criteria ($H_{\rm{AB}}>27\,\rm{mag}$ and $H_{\rm{AB}}-\rm{F444W}>2.3$). These simulated HST-dark galaxies lie at high redshifts ($z=4-7$), have high levels of dust attenuation ($A_{V}=2-4$), and display compact recent star formation ($R_{1/2,\,\rm{4.4\,\mu\rm{m}}}\lesssim1\,\rm{kpc}$). Orientation is very important: for all but one of the 17 simulated galaxy snapshots with HST-dark sightlines, there exist other sightlines that do not meet the criteria. This result has important implications for comparisons between observations and models that do not resolve the detailed star-dust geometry, such as semi-analytic models or coarsely-resolved hydrodynamical simulations. Critically, we demonstrate that HST-dark sources are not an unexpected or exotic population, but a subset of high-redshift, highly-dust-attenuated sources viewed along certain lines of sight.Comment: 12 pages, 8 figures. Accepted for publication in Ap

An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks

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

Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation

We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and Prospector codes to fit 6,706 synthetic spectral energy distributions (SEDs) of simulated massive galaxies at $1 < z < 8$ from the Feedback in Realistic Environments (FIRE) project. We identify a previously unknown systematic error in the Magphys results due to bursty star formation: the SFR estimates of individual galaxies can differ from the true values by as much as 1 dex, at large statistical significance ($>5\sigma$), depending on the details of their recent SFH. The SFRs inferred using Prospector do not exhibit this trend, likely because unlike Magphys, Prospector uses non-parametric SFHs. We urge caution when using Magphys, or other codes assuming parametric SFHs, to study galaxies where the average SFR may have changed significantly over the last $\sim$100 Myr, such as those which have recently quenched their star formation or those experiencing an ongoing burst. This concern is especially relevant, for example, when fitting JWST observations of very high-redshift galaxies.Comment: 5 pages, 3 figures, submitted to MNRAS Letter

Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation

© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and Prospector codes to fit 6,706 synthetic spectral energy distributions of simulated massive galaxies at $1 5\sigma$), depending on the details of their recent SFH. SFRs inferred using Prospector with non-parametric SFHs do not exhibit this trend. We show that using parametric SFHs (pSFHs) causes SFR uncertainties to be underestimated by a factor of up to $5\times$. Although this undoubtedly contributes to the significance of the systematic, it cannot explain the largest biases in the SFRs of the starbursting galaxies, which could be caused by details of the stochastic prior sampling or the burst implementation in the Magphys libraries. We advise against using pSFHs and urge careful consideration of starbursts when SED modelling galaxies where the SFR may have changed significantly over the last ~100 Myr, such as recently quenched galaxies, or those experiencing a burst. This concern is especially relevant, e.g. when fitting JWST observations of very high-redshift galaxies.Peer reviewe

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

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: 40 pages, 22 figures (11 made of subfigures