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Daniel Angles-Alcazar

Publications and source records attributed to Daniel Angles-Alcazar.

35 records · Page 2Linked to original sources

Why do Black Holes Trace Bulges (& Central Surface Densities), Instead of Galaxies as a Whole?

Previous studies of fueling black holes (BHs) in galactic nuclei have argued (on scales ~0.01-1000pc) accretion is dynamical with inflow rates $\dot{M}\simη\,M_{\rm gas}/t_{\rm dyn}$ in terms of gas mass $M_{\rm gas}$, dynamical time $t_{\rm dyn}$, and some $η$. But these models generally neglected expulsion of gas by stellar feedback, or considered extremely high densities where expulsion is inefficient. Studies of star formation, however, have shown on sub-kpc scales the expulsion efficiency $f_{\rm wind}=M_{\rm ejected}/M_{\rm total}$ scales with the gravitational acceleration as $(1-f_{\rm wind})/f_{\rm wind}\sim\bar{a}_{\rm grav}/\langle\dot{p}/m_{\ast}\rangle\sim Σ_{\rm eff}/Σ_{\rm crit}$ where $\bar{a}_{\rm grav}\equiv G\,M_{\rm tot}(<r)/r^{2}$ and $\langle\dot{p}/m_{\ast}\rangle$ is the momentum injection rate from young stars. Adopting this as the simplest correction for stellar feedback, $η\rightarrow η\,(1-f_{\rm wind})$, we show this provides a more accurate description of simulations with stellar feedback at low densities. This has immediate consequences, predicting e.g. the slope and normalization of the $M-σ$ and $M-M_{\rm bulge}$ relation, $L_{\rm AGN}-$SFR relations, and explanations for outliers in compact Es. Most strikingly, because star formation simulations show expulsion is efficient ($f_{\rm wind}\sim1$) below total-mass surface density $M_{\rm tot}/π\,r^{2}<Σ_{\rm crit}\sim3\times10^{9}\,M_{\odot}\,{\rm kpc^{-2}}$ (where $Σ_{\rm crit}=\langle\dot{p}/m_{\ast}\rangle/(π\,G)$), BH mass is predicted to specifically trace host galaxy properties above a critical surface brightness $Σ_{\rm crit}$ (B-band $μ_{\rm B}^{\rm crit}\sim 19\,{\rm mag\,arcsec^{-2}}$). This naturally explains why BH masses preferentially reflect bulge properties or central surface-densities ($Σ_{1\,{\rm kpc}}$), not 'total' galaxy properties.

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FIRE-3: Updated Stellar Evolution Models, Yields, & Microphysics and Fitting Functions for Applications in Galaxy Simulations

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.

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Galaxies lacking dark matter produced by close encounters in a cosmological simulation

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.

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Co-evolution of massive black holes and their host galaxies at high redshift: discrepancies from six cosmological simulations and the key role of JWST

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.

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The Circumgalactic Medium from the CAMELS Simulations: Forecasting Constraints on Feedback Processes from Future Sunyaev-Zeldovich Observations

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.

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The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span $\sim$100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

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Robust marginalization of baryonic effects for cosmological inference at the field level

We train neural networks to perform likelihood-free inference from $(25\,h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales ($\gtrsim 100\,h^{-1}{\rm kpc}$) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of $Ω_{\rm m} (\pm 4\%)$ and $σ_8 (\pm 2.5\%)$ from simulations completely different to the ones used to train it.

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Inpainting hydrodynamical maps with deep learning

From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15\%$ or $30\%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1\%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $\sim5\%$ when covering $15\%$ of the maps while the results become unreliable when $30\%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.

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Finding universal relations in subhalo properties with artificial intelligence

We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star-formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. We interpret the relation found by the neural network and approximated by the analytic equation as being connected to the virial theorem.

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The Radio Galaxy Population in the SIMBA SImulations

We examine the 1.4GHz radio luminosities of galaxies arising from star formation and active galactic nuclei (AGN) within the state-of-the-art cosmological hydrodynamic simulation Simba. Simba grows black holes via gravitational torque limited accretion from cold gas and Bondi accretion from hot gas, and employs AGN feedback including jets at low Eddington ratios. We define a population of radio loud AGN (RLAGN) based on the presence of ongoing jet feedback. Within RLAGN we define high and low excitation radio galaxies (HERGs and LERGs) based on their dominant mode of black hole accretion: torque limited accretion representing feeding from a cold disk, or Bondi representing advection-dominated accretion from a hot medium. Simba predicts good agreement with the observed radio luminosity function (RLF) and its evolution, overall as well as separately for HERGs and LERGs. Quiescent galaxies with AGN-dominated radio flux dominate the RLF at > $\sim 10^{22-23}$ W Hz$^{-1}$, while star formation dominates at lower radio powers. Overall, RLAGN have higher black hole accretion rates and lower star formation rates than non-RLAGN at a given stellar mass or velocity dispersion, but have similar black hole masses. Simba predicts a LERG number density of 8.53 Mpc$^{-3}$, $\sim 10\times$ higher than for HERGs, broadly as observed. While LERGs dominate among most massive galaxies with the largest black holes and HERGs dominate at high specific star formation rates, they otherwise largely populate similar-sized dark matter halos and have similar host galaxy properties. Simba thus predicts that deeper radio surveys will reveal an increasing overlap between the host galaxy demographics of HERGs and LERGs.

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Realistic mock observations of the sizes and stellar mass surface densities of massive galaxies in FIRE-2 zoom-in simulations

The galaxy size-stellar mass and central surface density-stellar mass relationships are observational constraints on galaxy formation models. However, inferring the physical size of a galaxy from observed stellar emission is non-trivial due to various observational effects. Consequently, forward-modeling light-based sizes from simulations is desirable. In this work, we use the {\skirt} dust radiative transfer code to generate synthetic observations of massive galaxies ($M_{*}\sim10^{11}\,\rm{M_{\odot}}$ at $z=2$, hosted by haloes of mass $M_{\rm{halo}}\sim10^{12.5}\,\rm{M_{\odot}}$) from high-resolution cosmological zoom-in simulations that form part of the Feedback In Realistic Environments (FIRE) project. The simulations used in this paper include explicit stellar feedback but no active galactic nucleus (AGN) feedback. From each mock observation, we infer the effective radius ($R_e$), as well as the stellar mass surface density within this radius and within $1\,\rm{kpc}$ ($Σ_e$ and $Σ_1$, respectively). We first investigate how well the intrinsic half-mass radius and stellar mass surface density can be inferred from observables. The predicted sizes and surface densities are within a factor of two of the intrinsic values. We then compare our predictions to the observed size-mass relationship and the $Σ_1-M_\star$ and $Σ_e-M_\star$ relationships. At $z\gtrsim2$, the simulated massive galaxies are in general agreement with observational scaling relations. At $z\lesssim2$, they evolve to become too compact but still star-forming, in the stellar mass and redshift regime where many of them should be quenched. Our results suggest that some additional source of feedback, such as AGN driven outflows, is necessary in order to decrease the central densities of the simulated massive galaxies to bring them into agreement with observations at $z\lesssim2$.

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Cosmological baryon transfer in the SIMBA simulations

We present a framework for characterizing the large scale movement of baryons relative to dark matter in cosmological simulations, requiring only the initial conditions and final state of the simulation. This is performed using the spread metric which quantifies the distance in the final conditions between initially neighbouring particles, and by analysing the baryonic content of final haloes relative to that of the initial Lagrangian regions defined by their dark matter component. Applying this framework to the SIMBA cosmological simulations, we show that 40% (10%) of cosmological baryons have moved $> 1h^{-1}~ {\rm Mpc}^{-1}$ ($3h^{-1}~ {\rm Mpc}^{-1}$) by $z=0$, due primarily to entrainment of gas by jets powered by AGN, with baryons moving up to $12h^{-1}~ {\rm Mpc}^{-1}$ away in extreme cases. Baryons decouple from the dynamics of the dark matter component due to hydrodynamic forces, radiative cooling, and feedback processes. As a result, only 60% of the gas content in a given halo at $z=0$ originates from its Lagrangian region, roughly independent of halo mass. A typical halo in the mass range $M_{\rm vir} = 10^{12}$--$10^{13}{\rm M}_\odot$ only retains 20% of the gas originally contained in its Lagrangian region. We show that up to 20% of the gas content in a typical Milky Way mass halo may originate in the region defined by the dark matter of another halo. This inter-Lagrangian baryon transfer may have important implications for the origin of gas and metals in the circumgalactic medium of galaxies, as well as for semi-analytic models of galaxy formation and "zoom-in" simulations.

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FIRE-2 Simulations: Physics versus Numerics in Galaxy Formation

The Feedback In Realistic Environments (FIRE) project explores feedback in cosmological galaxy formation simulations. Previous FIRE simulations used an identical source code (FIRE-1) for consistency. Motivated by the development of more accurate numerics - including hydrodynamic solvers, gravitational softening, and supernova coupling algorithms - and exploration of new physics (e.g. magnetic fields), we introduce FIRE-2, an updated numerical implementation of FIRE physics for the GIZMO code. We run a suite of simulations and compare against FIRE-1: overall, FIRE-2 improvements do not qualitatively change galaxy-scale properties. We pursue an extensive study of numerics versus physics. Details of the star-formation algorithm, cooling physics, and chemistry have weak effects, provided that we include metal-line cooling and star formation occurs at higher-than-mean densities. We present new resolution criteria for high-resolution galaxy simulations. Most galaxy-scale properties are robust to numerics we test, provided: (1) Toomre masses are resolved; (2) feedback coupling ensures conservation, and (3) individual supernovae are time-resolved. Stellar masses and profiles are most robust to resolution, followed by metal abundances and morphologies, followed by properties of winds and circum-galactic media (CGM). Central (~kpc) mass concentrations in massive (L*) galaxies are sensitive to numerics (via trapping/recycling of winds in hot halos). Multiple feedback mechanisms play key roles: supernovae regulate stellar masses/winds; stellar mass-loss fuels late star formation; radiative feedback suppresses accretion onto dwarfs and instantaneous star formation in disks. We provide all initial conditions and numerical algorithms used.

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Metal flows of the circumgalactic medium, and the metal budget in galactic halos

We present an analysis of the flow of metals through the circumgalactic medium (CGM) in the Feedback in Realistic Environments (FIRE) simulations of galaxy formation, ranging from isolated dwarfs to $L*$ galaxies. We find that nearly all metals produced in high-redshift galaxies are carried out in winds that reach $0.25 R_{\rm vir}$. When measured at $0.25 R_{\rm vir}$ the metallicity of outflows is slightly higher than the interstellar medium (ISM) metallicity. Many metals thus reside in the CGM. Cooling and recycling from this reservoir determine the metal budget in the ISM. The outflowing metal flux decreases by a factor of $\sim2-5$ between $0.25 R_{\rm vir}$ and $R_{\rm vir}$. Furthermore, outflow metallicity is typically lower at $R_{\rm vir}$ owing to dilution of the remaining outflow by metal-poor material swept up from the CGM. The inflow metallicity at $R_{\rm vir}$ is generally low, but outflow and inflow metallicities are similar in the inner halo. At low redshift, massive galaxies no longer generate outflows that reach the CGM, causing a divergence in CGM and ISM metallicity. Dwarf galaxies continue to generate outflows, although they preferentially retain metal ejecta. In all but the least massive galaxy considered, a majority of the metals are within the halo at $z=0$. We measure the fraction of metals in CGM, ISM and stars, and quantify the thermal state of CGM metals in each halo. The total amount of metals in the low-redshift CGM of two simulated $L*$ galaxies is consistent with estimates from the COS halos survey, while for the other two it appears to be lower.

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The Structure and Dynamical Evolution of the Stellar Disk of a Simulated Milky Way-Mass Galaxy

We study the structure, age and metallicity gradients, and dynamical evolution using a cosmological zoom-in simulation of a Milky Way-mass galaxy from the Feedback in Realistic Environments project. In the simulation, stars older than 6 Gyr were formed in a chaotic, bursty mode and have the largest vertical scale heights (1.5-2.5 kpc) by z=0, while stars younger than 6 Gyr were formed in a relatively calm, stable disk. The vertical scale height increases with stellar age at all radii, because (1) stars that formed earlier were thicker "at birth", and (2) stars were kinematically heated to an even thicker distribution after formation. Stars of the same age are thicker in the outer disk than in the inner disk (flaring). These lead to positive vertical age gradients and negative radial age gradients. The radial metallicity gradient is neg- ative at the mid-plane, flattens at larger disk height |Z|, and turns positive above |Z|~1.5kpc. The vertical metallicity gradient is negative at all radii, but is steeper at smaller radii. These trends broadly agree with observations in the Milky Way and can be naturally understood from the age gradients. The vertical stellar density profile can be well-described by two components, with scale heights 200-500 pc and 1-1.5 kpc, respectively. The thick component is a mix of stars older than 4 Gyr which formed through a combination of several mechanisms. Our results also demonstrate that it is possible to form a thin disk in cosmological simulations even with strong stellar feedback.

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The BLAST Survey of the Vela Molecular Cloud: Dynamical Properties of the Dense Cores in Vela-D

The Vela-D region, according to the nomenclature given by Murphy & May (1991), of the star forming complex known as the Vela Molecular Ridge (VMR), has been recently analyzed in details by Olmi et al. (2009), who studied the physical properties of 141 pre- and proto-stellar cold dust cores, detected by the ``Balloon-borne Large-Aperture Submillimeter Telescope'' (BLAST) during a much larger (55 sq. degree) Galactic Plane survey encompassing the whole VMR. This survey's primary goal was to identify the coldest, dense dust cores possibly associated with the earliest phases of star formation. In this work, the dynamical state of the Vela-D cores is analyzed. Comparison to dynamical masses of a sub-sample of the Vela-D cores estimated from the 13CO survey of Elia et al. (2007), is complicated by the fact that the 13CO linewidths are likely to trace the lower density intercore material, in addition to the dense gas associated with the compact cores observed by BLAST. In fact, the total internal pressure of these cores, if estimated using the 13CO linewidths, appears to be higher than the cloud ambient pressure. If this were the case, then self-gravity and surface pressure would be insufficient to bind these cores and an additional source of external confinement (e.g., magnetic field pressure) would be required. However, if one attempts to scale down the 13CO linewidths, according to the observations of high-density tracers in a small sample of sources, then most proto-stellar cores would result effectively gravitationally bound.

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The BLAST Survey of the Vela Molecular Cloud: Physical Properties of the Dense Cores in Vela-D

The Balloon-borne Large-Aperture Submillimeter Telescope (BLAST) carried out a 250, 350 and 500 micron survey of the galactic plane encompassing the Vela Molecular Ridge, with the primary goal of identifying the coldest dense cores possibly associated with the earliest stages of star formation. Here we present the results from observations of the Vela-D region, covering about 4 square degrees, in which we find 141 BLAST cores. We exploit existing data taken with the Spitzer MIPS, IRAC and SEST-SIMBA instruments to constrain their (single-temperature) spectral energy distributions, assuming a dust emissivity index beta = 2.0. This combination of data allows us to determine the temperature, luminosity and mass of each BLAST core, and also enables us to separate starless from proto-stellar sources. We also analyze the effects that the uncertainties on the derived physical parameters of the individual sources have on the overall physical properties of starless and proto-stellar cores, and we find that there appear to be a smooth transition from the pre- to the proto-stellar phase. In particular, for proto-stellar cores we find a correlation between the MIPS24 flux, associated with the central protostar, and the temperature of the dust envelope. We also find that the core mass function of the Vela-D cores has a slope consistent with other similar (sub)millimeter surveys.

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