arXiv subjects

Daniel Anglés-Alcázar

Publications and source records attributed to Daniel Anglés-Alcázar.

At least 73 records · Page 4Linked to original sources

arXiv2022

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

astro-ph.CO↗

arXiv2021

Weighing the Milky Way and Andromeda with Artificial Intelligence

We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods.

astro-ph.GA↗

arXiv2021

Bringing faint active galactic nuclei (AGNs) to light: a view from large-scale cosmological simulations

The sensitivity of X-ray facilities and our ability to detect fainter active galactic nuclei (AGNs) will increase with the upcoming Athena mission and the AXIS and Lynx concept missions, thus improving our understanding of supermassive black holes (BHs) in a luminosity regime that can be dominated by X-ray binaries. We analyze the population of faint AGN (L_x (2-10 keV) < 10^42 erg/s) in the Illustris, TNG100, EAGLE, and SIMBA cosmological simulations, and find that the properties of their host galaxies vary from one simulation to another. In Illustris and EAGLE, faint AGN are powered by low-mass BHs located in low-mass star-forming galaxies. In TNG100 and SIMBA, they are mostly associated with more massive BHs in quenched massive galaxies. We model the X-ray binary populations (XRB) of the simulated galaxies, and find that AGN often dominate the galaxy AGN+XRB hard X-ray luminosity at z>2, while XRBs dominate in some simulations at z<2. Whether the AGN or XRB emission dominates in star-forming and quenched galaxies depends on the simulations. These differences in simulations can be used to discriminate between galaxy formation models with future high-resolution X-ray observations. We compare the luminosity of simulated faint AGN host galaxies to observations of stacked galaxies from Chandra. Our comparison indicates that the simulations post-processed with our X-ray modeling tend to overestimate the AGN+XRB X-ray luminosity; luminosity that can be strongly affected by AGN obscuration. Some simulations reveal clear AGN trends as a function of stellar mass (e.g., galaxy luminosity drop in massive galaxies), which are not apparent in the observations.

astro-ph.GA↗

arXiv2021

Simba: The average properties of the circumgalactic medium of $2 \leq z \leq 3$ quasars are determined primarily by stellar feedback

We use the Simba cosmological hydrodynamic simulation suite to explore the impact of feedback on the circumgalactic medium (CGM) and intergalactic medium (IGM) around $2 \leq z \leq 3$ quasars. We identify quasars in Simba as the most rapidly-accreting black holes, and show that they are well-matched in bolometric luminosity and correlation strength to real quasars. We extract Lyman-alpha (Ly-a) absorption in spectra passing at different transverse distances (10 kpc $\lesssim b \lesssim$ 10 Mpc) around those quasars, and compare to observations of the mean Ly-a absorption profile. The observations are well reproduced, except within 100 kpc from the foreground quasar, where Simba overproduces absorption; this could potentially be mitigated by including ionisation from the quasar itself. By comparing runs with different feedback modules activated, we find that (mechanical) AGN feedback has little impact on the surrounding CGM even around these most highly luminous black holes, while stellar feedback has a significant impact. By further investigating thermodynamic and kinematic properties of CGM gas, we find that stellar feedback, and not AGN feedback, is the primary physical driver in determining the average properties of the CGM around $z\sim 2-3$ quasars. We also compare our results with previous works, and find that Simba predicts much more absorption within 100 kpc than the Nyx and Illustris simulations, showing that the Ly-a absorption profile can be a powerful constraint on simulations. Instruments such as VLT-MUSE and upcoming surveys (e.g., WEAVE and DESI) promise to further improve such constraints.

astro-ph.GA↗

arXiv2021

Supermassive black holes in cosmological simulations II: the AGN population and predictions for upcoming X-ray missions

In large-scale hydrodynamical cosmological simulations, the fate of massive galaxies is mainly dictated by the modeling of feedback from active galactic nuclei (AGN). The amount of energy released by AGN feedback is proportional to the mass that has been accreted onto the BHs, but the exact sub-grid modeling of AGN feedback differs in all simulations. Whilst modern simulations reliably produce populations of quiescent massive galaxies at z<2, it is also crucial to assess the similarities and differences of the responsible AGN populations. Here, we compare the AGN population of the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA simulations. The AGN luminosity function (LF) varies significantly between simulations. Although in agreement with current observational constraints at z=0, at higher redshift the agreement of the LFs deteriorates with most simulations producing too many AGN of L_{x, 2-10 keV}~10^43-10^44 erg/s. AGN feedback in some simulations prevents the existence of any bright AGN with L_{x, 2-10 keV}>=10^45 erg/s (although this is sensitive to AGN variability), and leads to smaller fractions of AGN in massive galaxies than in the observations at z<=2. We find that all the simulations fail at producing a number density of AGN in good agreement with observational constraints for both luminous (L_{x, 2-10 keV}~10^43-10^45 erg/s) and fainter (L_{x, 2-10 keV}~10^42-10^43 erg/s) AGN, and at both low and high redshift. These differences can aid us in improving future BH and galaxy subgrid modeling in simulations. Upcoming X-ray missions (e.g., Athena, AXIS, and LynX) will bring faint AGN to light and new powerful constraints. After accounting for AGN obscuration, we find that the predicted number density of detectable AGN in future surveys spans at least one order of magnitude across the simulations, at any redshift.

astro-ph.GA↗

arXiv2021

Supermassive black holes in cosmological simulations I: M_BH-M_star relation and black hole mass function

The past decade has seen significant progress in understanding galaxy formation and evolution using large-scale cosmological simulations. While these simulations produce galaxies in overall good agreement with observations, they employ different sub-grid models for galaxies and supermassive black holes (BHs). We investigate the impact of the sub-grid models on the BH mass properties of the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA simulations, focusing on the M_BH-M_star relation and the BH mass function. All simulations predict tight M_BH-M_star relations, and struggle to produce the lowest (M_BH< 10^7.5 Msun) in galaxies of M_star~10^10.5-10^11.5 Msun. While the time evolution of the mean M_BH-M_star relation is mild (<1 dex in BH mass for 0 10^9 Msun in most of the simulations. The BH mass function is dominated by efficiently accreting BHs (log10 f_Edd >-2$) at high redshifts, and transitions progressively from the high-mass to the low-mass end to be governed by inactive BHs. The transition time and the contribution of active BHs are different among the simulations, and can be used to evaluate models against observations.

astro-ph.GA↗

arXiv2021

Multifield Cosmology with Artificial Intelligence

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $Ω_{\rm m}$ and $σ_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $Ω_{\rm m}$ with higher accuracy than the maps from the N-body simulations.

astro-ph.CO↗

arXiv2021

Seeds Don't Sink: Even Massive Black Hole "Seeds" Cannot Migrate to Galaxy Centers Efficiently

Possible formation scenarios of supermassive black holes in the early universe include rapid growth from less massive seed black holes (BHs) via super-Eddington accretion or runaway mergers, yet both of these scenarios would require seed BHs to efficiently sink to and be trapped in the galactic center via dynamical friction. This may not be true for their complicated dynamics in clumpy high-$z$ galaxies. In this work we study this "sinking problem" with state-of-the-art high-resolution cosmological simulations, combined with both direct $N$-body integration of seed BH trajectories and post-processing of randomly generated test particles with a newly developed dynamical friction estimator. We find that seed BHs less massive than $10^8\,M_\odot$ (i.e., all but the already-supermassive seeds) cannot efficiently sink in typical high-$z$ galaxies. We also discuss two possible solutions: dramatically increasing the number of seeds such that one seed can end up trapped in the galactic center by chance, or seed BHs being embedded in dense structures (e.g. star clusters) with effective masses above the mass threshold. We discuss the limitations of both solutions.

astro-ph.GA↗

arXiv2021

Characterizing mass, momentum, energy and metal outflow rates of multi-phase galactic winds in the FIRE-2 cosmological simulations

We characterize mass, momentum, energy and metal outflow rates of multi-phase galactic winds in a suite of FIRE-2 cosmological "zoom-in" simulations from the Feedback in Realistic Environments (FIRE) project. We analyze simulations of low-mass dwarfs, intermediate-mass dwarfs, Milky Way-mass halos, and high-redshift massive halos. Consistent with previous work, we find that dwarfs eject about 100 times more gas from their interstellar medium (ISM) than they form in stars, while this mass "loading factor" drops below one in massive galaxies. Most of the mass is carried by the hot phase ($>10^5$ K) in massive halos and the warm phase ($10^3-10^5$ K) in dwarfs; cold outflows ($<10^3$ K) are negligible except in high-redshift dwarfs. Energy, momentum and metal loading factors from the ISM are of order unity in dwarfs and significantly lower in more massive halos. Hot outflows have $2-5\times$ higher specific energy than needed to escape from the gravitational potential of dwarf halos; indeed, in dwarfs, the mass, momentum, and metal outflow rates increase with radius whereas energy is roughly conserved, indicating swept up halo gas. Burst-averaged mass loading factors tend to be larger during more powerful star formation episodes and when the inner halo is not virialized, but we see effectively no trend with the dense ISM gas fraction. We discuss how our results can guide future controlled numerical experiments that aim to elucidate the key parameters governing galactic winds and the resulting associated preventative feedback.

astro-ph.GA↗

arXiv2021

The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations

We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of $(25~h^{-1}{\rm Mpc})^3$ volume each: 2,184 state-of-the-art (magneto-)hydrodynamic simulations run with the AREPO and GIZMO codes, employing the same baryonic subgrid physics as the IllustrisTNG and SIMBA simulations, and 2,049 N-body simulations. The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. CAMELS contains thousands of different cosmological and astrophysical models by way of varying $Ω_m$, $σ_8$, and four parameters controlling stellar and AGN feedback, following the evolution of more than 100 billion particles and fluid elements over a combined volume of $(400~h^{-1}{\rm Mpc})^3$. We describe the simulations in detail and characterize the large range of conditions represented in terms of the matter power spectrum, cosmic star formation rate density, galaxy stellar mass function, halo baryon fractions, and several galaxy scaling relations. We show that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum. This emphasizes the need for marginalizing over baryonic effects to extract the maximum amount of information from cosmological surveys. We illustrate the unique potential of CAMELS using several machine learning applications, including non-linear interpolation, parameter estimation, symbolic regression, data generation with Generative Adversarial Networks (GANs), dimensionality reduction, and anomaly detection.

astro-ph.CO↗

arXiv2021

Neutral CGM as damped Lyα absorbers at high redshift

Recent searches for the hosts of high-redshift ($z \sim 4$) damped Ly$α$ absorbers (DLAs) have detected bright galaxies at distances of tens of kpc from the DLA. Using the FIRE-2 cosmological zoom simulations, we argue that these relatively large distances are due to a predominantly cool and neutral inner circumgalactic medium (CGM) surrounding high-redshift galaxies. The inner CGM is cool because of the short cooling time of hot gas in $\lesssim10^{12}$ Msun halos, which implies that accretion and feedback energy are radiated quickly, while it is neutral due to the high volume densities and column densities at high redshift which shield cool gas from photoionization. Our analysis predicts large DLA covering factors ($\gtrsim50\%$) out to impact parameters $\sim0.3((1 + z)/5)^{3/2}\ R_{\rm vir}$ from the central galaxies at $z > 1$, equivalent to a physical distance of $\sim 21 M_{12}^{1/3} ((1 + z)/5)^{1/2}$ kpc ($R_{\rm vir}$ and $M_{12}$ are the halo virial radius and mass in units of $10^{12}$ Msun, respectively). This implies that DLA covering factors at $z \sim 4$ may be comparable to unity out to a distance $\sim 10$ times larger than stellar half-mass radii. A predominantly neutral inner CGM in the early universe suggests that its mass and metallicity can be directly constrained by CGM absorption surveys, without resorting to large ionization corrections as required for ionized CGM.

astro-ph.GA↗

arXiv2021

The origin of galaxy colour bimodality in the scatter of the Stellar-to-Halo Mass Relation

Recent observations reveal that, at a given stellar mass, blue galaxies tend to live in haloes with lower mass while red galaxies live in more massive host haloes. The physical driver behind this is still unclear because theoretical models predict that, at the same halo mass, galaxies with high stellar masses tend to live in early-formed haloes which naively leads to an opposite trend. Here, we show that the {\sc Simba} simulation quantitatively reproduces the colour bimodality in SHMR and reveals an inverse relationship between halo formation time and galaxy transition time. It suggests that the origin of this bimodality is rooted in the intrinsic variations of the cold gas content due to halo assembly bias. {\sc Simba}'s SHMR bimodality quantitatively relies on two aspects of its input physics: (1) Jet-mode AGN feedback, which quenches galaxies and sets the qualitative trend; and (2) X-ray AGN feedback, which fully quenches galaxies and yields better agreement with observations. The interplay between the growth of cold gas and the AGN quenching in {\sc Simba} results in the observed SHMR bimodality.

astro-ph.GA↗

arXiv2021

IQ Collaboratory III: The Empirical Dust Attenuation Framework -- Taking Hydrodynamical Simulations with a Grain of Dust

We present the Empirical Dust Attenuation (EDA) framework -- a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color-magnitude relations, $(g-r) - M_r$ and $(FUV-NUV)-M_r$, of the simulations to a $M_r < -20$ and UV complete SDSS galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color-magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation-slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low $A_V$ since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations.

astro-ph.GA↗

arXiv2021

Cosmological simulations of quasar fueling to sub-parsec scales using Lagrangian hyper-refinement

We present cosmological hydrodynamic simulations of a quasar-mass halo ($M_{\rm halo} \approx 10^{12.5}\,{\rm M}_{\odot}$ at z=2) that for the first time resolve gas transport down to the inner 0.1 pc surrounding the central massive black hole. We model a multi-phase interstellar medium including stellar feedback by supernovae, stellar winds, and radiation, and a hyper-Lagrangian refinement technique increasing the resolution dynamically approaching the black hole. We do not include black hole feedback. We show that the sub-pc inflow rate (1) can reach ~6 M$_{\odot}$yr$^{-1}$ roughly in steady state during the epoch of peak nuclear gas density (z~2), sufficient to power a luminous quasar, (2) is highly time variable in the pre-quasar phase, spanning 0.001-10 M$_{\odot}$yr$^{-1}$ on Myr timescales, and (3) is limited to short (~2 Myr) active phases (0.01-0.1 M$_{\odot}$yr$^{-1}$) followed by longer periods of inactivity at lower nuclear gas density and late times (z~1), owing to the formation of a hot central cavity. Inflowing gas is primarily cool, rotational support dominates over turbulence and thermal pressure, and star formation can consume as much gas as provided by inflows across 1 pc - 10 kpc. Gravitational torques from multi-scale stellar non-axisymmetries dominate angular momentum transport over gas self-torquing and pressure gradients, with accretion weakly dependent on black hole mass. Sub-pc inflow rates correlate with nuclear (but decouple from global) star formation and can exceed the Eddington rate by x10. The black hole can move ~10 pc from the galaxy center on ~0.1 Myr. Accreting gas forms pc-scale, rotationally supported, obscuring structures often misaligned with the galaxy-scale disk. These simulations open a new avenue to investigate black hole-galaxy co-evolution.

astro-ph.GA↗

arXiv2021

Which AGN Jets Quench Star Formation in Massive Galaxies?

Without additional heating, radiative cooling of gas in the halos of massive galaxies (Milky Way and above) produces cold gas or stars in excess of that observed. Previous work suggested that AGN jets are likely required, but the form of jet energy required to quench remains unclear. This is particularly challenging for galaxy simulations, in which the resolution is orders of magnitude coarser than necessary to form and evolve the jet. On such scales, the uncertain parameters include: jet energy form (kinetic, thermal, and cosmic ray (CR) energy), energy, momentum, and mass flux, magnetic field strength and geometry, jet precession angle and period, opening-angle, and duty cycle. We investigate all of these parameters in a $10^{14}\,{\rm M}_{\odot}$ halo using high-resolution non-cosmological MHD simulations with the FIRE-2 (Feedback In Realistic Environments) stellar feedback model, conduction, and viscosity. We explore which scenarios match observational constraints and show that CR-dominated jets can most efficiently quench the central galaxy through a combination of CR pressure support and a modification of the thermal instability. Jets with most energy in mildly relativistic ($\sim$ MeV or $\sim10^{10}$ K) thermal plasma work, but require a factor $\sim 10$ larger energy input. For a fixed energy flux, jets with higher specific energy (longer cooling times) quench more effectively. For this halo size, kinetic jets are less efficient in quenching unless they have wide opening or precession angles. Magnetic fields play a minor role except when the magnetic flux reaches $\gtrsim 10^{44}$ erg s$^{-1}$ in a kinetic jet model, which causes the jet cocoon to significantly widen, and the quenching to become explosive. We conclude that the criteria for a successful jet model are an optimal energy flux and a sufficiently wide jet cocoon with long enough cooling time at the cooling radius.

astro-ph.GA↗

arXiv2020

Virialization of the inner CGM in the FIRE simulations and implications for galaxy discs, star formation and feedback

We use the FIRE-2 cosmological simulations to study the formation of a quasi-static, virial-temperature gas phase in the circumgalactic medium (CGM) at redshifts 0<z<5, and how the formation of this virialized phase affects the evolution of galactic discs. We demonstrate that when the halo mass crosses ~10^12 M_sun, the cooling time of shocked gas in the inner CGM (~0.1 R_vir, where R_vir is the virial radius) exceeds the local free-fall time. The inner CGM then experiences a transition from on average sub-virial temperatures (T<<T_vir), large pressure fluctuations and supersonic inflow/outflow velocities, to virial temperatures (T~T_vir), uniform pressures and subsonic velocities. This transition occurs when the outer CGM (~0.5 R_vir) is already subsonic and has a temperature ~T_vir, indicating that the longer cooling times at large radii allow the outer CGM to virialize at lower halo masses than the inner CGM. This outside-in CGM virialization scenario is in contrast with inside-out scenarios commonly envisioned based on more idealized simulations. We demonstrate that inner CGM virialization coincides with abrupt changes in the central galaxy and its stellar feedback: the galaxy settles into a stable rotating disc, star formation transitions from `bursty' to `steady,' and stellar-driven galaxy-scale outflows are suppressed. Our results thus suggest that CGM virialization is initially associated with the formation of rotation-dominated thin galactic discs, rather than with the quenching of star formation as often assumed.

astro-ph.GA↗

arXiv2020

Neural networks as optimal estimators to marginalize over baryonic effects

Many different studies have shown that a wealth of cosmological information resides on small, non-linear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects.

astro-ph.CO↗

arXiv2020

Jet Feedback and the Photon Underproduction Crisis in Simba

We examine the impact of black hole jet feedback on the properties of the low-redshift intergalactic medium (IGM) in the SIMBA simulation, with a focus on the Ly$α$ forest mean flux decrement $D_A$. Without jet feedback, we confirm the Photon Underproduction Crisis (PUC) in which $Γ_{\rm HI}$ at $z=0$ must be increased by $\times6$ over the Haardt & Madau value in order to match the observed $D_{A}$. Turning on jet feedback lowers this discrepancy to $\sim\times 2.5$, and additionally using the recent Faucher-Giguère background mostly resolves the PUC, along with producing a flux probability distribution function in accord with observations. The PUC becomes apparent at late epochs ($z \lesssim 1$) where the jet and no-jet simulations diverge; at higher redshifts SIMBA reproduces the observed $D_{A}$ with no adjustment, with or without jets. The main impact of jet feedback is to lower the cosmic baryon fraction in the diffuse IGM from 39% to 16% at $z=0$, while increasing the warm-hot intergalactic medium (WHIM) baryon fraction from 30% to 70%; the lowering of the diffuse IGM content directly translates into a lowering of $D_{A}$ by a similar factor. Comparing to the older MUFASA simulation that employs different quenching feedback but is otherwise similar to SIMBA, MUFASA matches $D_{A}$ less well than SIMBA, suggesting that low-redshift measurements of $D_{A}$ and $Γ_{\rm HI}$ could provide constraints on feedback mechanisms. Our results suggest that widespread IGM heating at late times is a plausible solution to the PUC, and that SIMBA's jet AGN feedback model, included to quench massive galaxies, approximately yields this required heating.

astro-ph.GA↗