arXiv subjects

Daniel Anglés-Alcázar

Publications and source records attributed to Daniel Anglés-Alcázar.

At least 37 records · Page 2Linked to original sources

arXiv2025

Powerful nuclear outflows and circumgalactic medium shocks driven by the most luminous known obscured quasar in the Universe

We report integral field spectroscopy observations with the Near-Infrared Spectrograph on board JWST targeting the 60 kpc environment surrounding the most luminous obscured 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.

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arXiv2025

EMBER-2: Emulating baryons from dark matter across cosmic time with deep modulation networks

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

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arXiv2025

X-raying CAMELS: Constraining Baryonic Feedback in the Circum-Galactic Medium with the CAMELS simulations and eRASS X-ray Observations

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.

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arXiv2024

The impact of feedback on the evolution of gas density profiles from galaxies to clusters: a universal fitting formula from the Simba suite of simulations

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 $0<z<4$. We find that the radial profiles are well represented by a power law and that, for a fixed total halo mass, the slope and amplitude of such power law are generally weakly dependent on redshift. Once AGN-driven jets are activated in the simulation, the gas density profile of haloes with $M_{\rm 200c} \gtrsim 10^{13} \, \rm M_{\odot}$ declines more gently with radial distance. We argue that this distinctive feature could be exploited with current observations to discriminate amongst the predictions of the different feedback models. We introduce a universal fitting formula for the slope and amplitude of the gas density profile as a function of total halo mass and redshift. The best-fit functions are suitable for all feedback variants considered, and their predictions are in excellent agreement with the numerical results. We provide the values of all fit parameters, making our fitting formula a versatile tool to mimic the effect of Simba feedback models onto N-body simulations and semi-analytical models of galaxy formation. Our results can also aid observational estimates of the gas mass within haloes that assume a specific slope for the underlying gas density profile.

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arXiv2024

Large-scale dual AGN in large-scale cosmological hydrodynamical simulations

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.

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arXiv2024

Bridging Scales: Coupling the galactic nucleus to the larger cosmic environment

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*.

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arXiv2024

How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds

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.

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arXiv2024

Effects of multi-channel AGN feedback in FIRE cosmological simulations of massive galaxies

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 $M_{\rm BH}$-$σ$ 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.

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arXiv2024

The baryon cycle in modern cosmological hydrodynamical simulations

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.

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arXiv2024

Host-galaxy stars can dominate the ionizing radiation field of the circumgalactic medium in galaxies at Cosmic Noon

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.

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arXiv2024

Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations

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.

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arXiv2024

Dense stellar clump formation driven by strong quasar winds in the FIRE cosmological hydrodynamic simulations

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 $Σ_{\star} \sim 10^{4}\,{\rm M}_{\odot}\,{\rm pc}^{-2}$. 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 $Σ_{\rm SFR} \sim 10^{4}\,{\rm M}_{\odot}\,{\rm yr}^{-1}\,{\rm kpc}^{-2}$, 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.

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arXiv2024

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.

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arXiv2023

$\rm [C_{II}]$ 158 $\rm μm$ emission as an indicator of galaxy star formation rate

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.

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arXiv2023

An Exploration of AGN and Stellar Feedback Effects in the Intergalactic Medium via the Low Redshift Lyman-$α$ Forest

We explore the role of galactic feedback on the low redshift Lyman-$α$ (Ly$α$)~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$α$ 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$α$ forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-$z$ Ly$α$ 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.

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arXiv2023

Local positive feedback in the overall negative: the impact of quasar winds on star formation in the FIRE cosmological simulations

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.

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arXiv2023

Cosmological baryon spread and impact on matter clustering in CAMELS

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.

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arXiv2023

Robust Field-level Likelihood-free Inference with Galaxies

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 $Ω_{\rm m}$ 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 $\sim10~h^{-1}{\rm kpc}$.

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