Star Formation History of Dwarf Galaxies in Cosmological Hydrodynamic Simulations
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Kentaro Nagamine↗
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The robustness of cosmological hydrodynamic simulation predictions to changes in numerics and cooling physics
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Shuiyao Huang↗
Simulating Hydrodynamics in Cosmology with CRK-HACC
Abstract We introduce CRK-HACC, an extension of the Hardware/Hybrid Accelerated Cosmology Code (HACC), to resolve gas hydrodynamics in large-scale structure formation simulations of the universe. The new framework couples the HACC gravitational N -body solver with a modern smoothed-particle hydrodynamics (SPH) approach called conservative reproducing kernel SPH (CRKSPH). CRKSPH utilizes smoothing functions that exactly interpolate linear fields while manifestly preserving conservation laws (momentum, mass, and energy). The CRKSPH method has been incorporated to accurately model baryonic effects in cosmology simulations—an important addition targeting the generation of precise synthetic sky predictions for upcoming observational surveys. CRK-HACC inherits the codesign strategies of the HACC solver and is built to run on modern GPU-accelerated supercomputers. In this work, we summarize the primary solver components and present a number of standard validation tests to demonstrate code accuracy, including idealized hydrodynamic and cosmological setups, as well as self-similarity measurements.
79 ASTRONOMY AND ASTROPHYSICS↗
Reconstructing Ly$α$ Fields from Low-resolution Hydrodynamical Simulations with Deep Learning
Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly$α$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly$α$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly$α$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.
79 ASTRONOMY AND ASTROPHYSICS↗
Convergence of small scale Ly α structure at high- z under different reionization scenarios
ABSTRACT The Ly α forest (LAF) at z > 5 probes the thermal and reionization history of the intergalactic medium (IGM) and the nature of dark matter, but its interpretation requires comparison to cosmological hydrodynamical simulations. At high-z, convergence of these simulations is more exacting since transmission is dominated by underdense voids that are challenging to resolve. With evidence mounting for a late end to reionization, small structures down to the sub-kpc level may survive to later times than conventionally thought due to the reduced time for pressure smoothing to impact the gas, further tightening simulation resolution requirements. We perform a suite of simulations using the Eulerian cosmological hydrodynamics code Nyx, spanning domain sizes of 1.25 − 10 h−1 Mpc and 5 − 80 h−1 kpc cells, and explore the interaction of these variables with the timing of reionization on the properties of the matter distribution and the simulated LAF at z = 5.5. In observable Ly α power, convergence within 10 per cent is achieved for k < 0.1 s km–1, but larger k shows deviation of up to 20 per cent. While a later reionization retains more small structure in the density field, because of the greater thermal broadening there is little difference in the convergence of LAF power between early (z = 9) and later (z = 6) reionizations. We conclude that at z ∼ 5.5, resolutions of 10 kpc are necessary for convergence of LAF power at k < 0.1 s km–1, while higher-k modes require higher resolution, and that the timing of reionization does not significantly impact convergence given realistic photoheating.
79 ASTRONOMY AND ASTROPHYSICS↗
SDSS-IV MaNGA: Cannibalism Caught in the Act—On the Frequency of Occurrence of Multiple Cores in Brightest Cluster Galaxies
Although it is generally accepted that massive galaxies form in a two-phased fashion, beginning with a rapid mass buildup through intense starburst activities followed by primarily dry mergers that mainly deposit stellar mass at outskirts, the late time stellar mass growth of brightest cluster galaxies (BCGs), the most massive galaxies in the universe, is still not well understood. Several independent measurements have indicated a slower mass growth rate than predictions from theoretical models. We attempt to resolve the discrepancy by measuring the frequency of BCGs with multiple cores, which serve as a proxy of the merger rates in the central region and facilitate a more direct comparison with theoretical predictions. Using 79 BCGs at z = 0.06–0.15 with integral field spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) project, we obtain a multiple-core fraction of 0.11 ± 0.04 at z ≈ 0.1 within an 18 kpc radius from the center, which is comparable to the value of 0.08 ± 0.04 derived from mock observations of 218 simulated BCGs from the cosmological hydrodynamical simulation IllustrisTNG. We find that most cores that appear close to the BCGs from imaging data turn out to be physically associated systems. Anchoring on the similarity in the multiple-core frequency between the MaNGA and IllustrisTNG, we discuss the mass growth rate of BCGs over the past 4.5 Gyr.
79 ASTRONOMY AND ASTROPHYSICS↗
Simulating Hydrodynamics in Cosmology with CRK-HACC
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Nicholas Frontiere↗
Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations
We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM 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 (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ${(25\,{h}^{-1}\,{\rm{Mpc}})}^{3}$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM 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 DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM 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 DM on these scales.
cosmological parameters↗
Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window
ABSTRACT Cosmological hydrodynamical simulations provide valuable insights on galaxy evolution when coupled with observational data. Comparisons with real galaxies are typically performed via scaling relations of the observables. Here, we follow an alternative approach based on the spectral covariance in a model-independent way. We build upon previous work by Sharbaf et al. that studied the covariance of high-quality SDSS (Sloan Digital Sky Survey) continuum-subtracted spectra in a relatively narrow range of velocity dispersion ($\sigma \in [100,150]$ km s$^{-1}$). Here, the same analysis is applied to synthetic data from the eagle and IllustrisTNG100 simulations, to assess the ability of these runs to mimic real galaxies. The real and simulated spectra are consistent regarding spectral covariance, although with subtle differences that can inform the implementation of subgrid physics. Spectral fitting done a posteriori on stacks segregated with respect to latent space reveals that the first principal component (PC1) is predominantly influenced by the stellar age distribution, with an underlying age–metallicity degeneracy. Good agreement is found regarding star formation prescriptions but there is disagreement with active galactic nucleus (AGN) feedback, that also affects the subset of quiescent galaxies. We show a substantial difference in the implementation of the AGN subgrid prescriptions, regarding central black hole seeding, that could lead to the mismatch. Differences are manifest between these two simulations in the star formation histories stacked with respect to latent space. We emphasize that this methodology only relies on the spectral covariance to assess whether simulations provide a true representation of galaxy formation.
Sharbaf, Z. (ORCID:0009000450545946)↗
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
79 ASTRONOMY AND ASTROPHYSICS↗
Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
97 MATHEMATICS AND COMPUTING↗
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
79 ASTRONOMY AND ASTROPHYSICS↗
The X-Ray Halo Scaling Relations of Supermassive Black Holes
We carry out a comprehensive Bayesian correlation analysis between hot halos and direct masses of supermassive black holes (SMBHs), by retrieving the X-ray plasma properties (temperature, luminosity, density, pressure, and masses) over galactic to cluster scales for 85 diverse systems. We find new key scalings, with the tightest relation being −, followed by −. The tighter scatter (down to 0.2 dex) and stronger correlation coefficient of all the X-ray halo scalings compared with the optical counterparts (as the −) suggest that plasma halos play a more central role than stars in tracing and growing SMBHs (especially those that are ultramassive). Moreover, correlates better with the gas mass than dark matter mass. We show the important role of the environment, morphology, and relic galaxies/coronae, as well as the main departures from virialization/self-similarity via the optical/X-ray fundamental planes. We test the three major channels for SMBH growth: hot/Bondi-like models have inconsistent anticorrelation with X-ray halos and too low feeding; cosmological simulations find SMBH mergers as subdominant over most of cosmic time and too rare to induce a central-limit-theorem effect; the scalings are consistent with chaotic cold accretion, the rain of matter condensing out of the turbulent X-ray halos that sustains a long-term self-regulated feedback loop. The new correlations are major observational constraints for models of SMBH feeding/feedback in galaxies, groups, and clusters (e.g., to test cosmological hydrodynamical simulations), and enable the study of SMBHs not only through X-rays, but also via the Sunyaev–Zel'dovich effect (Compton parameter), lensing (total masses), and cosmology (gas fractions).
Supermassive black holes↗
Simulating Atomic Dark Matter in Milky Way Analogs
Dark sector theories naturally lead to multicomponent scenarios for dark matter where a subcomponent can dissipate energy through self-interactions, allowing it to efficiently cool inside galaxies. We present the first cosmological hydrodynamical simulations of Milky Way analogs where the majority of dark matter is collisionless cold dark matter (CDM) but a subcomponent (6%) is strongly dissipative minimal atomic dark matter (ADM). The simulations, implemented in GIZMO and utilizing FIRE-2 galaxy formation physics to model the standard baryonic sector, demonstrate that the addition of even a small fraction of dissipative dark matter can significantly impact galactic evolution despite being consistent with current cosmological constraints. We show that ADM gas with roughly standard model–like masses and couplings can cool to form a rotating “dark disk” with angular momentum closely aligned with the visible stellar disk. The morphology of the disk depends sensitively on the parameters of the ADM model, which affect the cooling rates in the dark sector. The majority of the ADM gas gravitationally collapses into dark “clumps” (regions of black hole or mirror star formation), which form a prominent bulge and a rotating thick disk in the central galaxy. These clumps form early and quickly sink to the inner ∼kiloparsec of the galaxy, affecting the galaxy’s star formation history and present-day baryonic and CDM distributions.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Peculiar velocity estimation from kinetic SZ effect using deep neural networks
ABSTRACT The Sunyaev–Zel’dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the independent estimation of the optical depth. Images of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to determine its ability of estimating peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17 per cent compared to the analytical approach.
Wang, Yuyu (ORCID:0000000202458547)↗
The AGORA High-resolution Galaxy Simulations Comparison Project. VIII. Disk Formation and Evolution of Simulated Milky Way Mass Galaxy Progenitors at 1 < z < 5
We investigate how differences in the stellar feedback produce disks with different morphologies in Milky Way–like progenitors over 1 ≤ z ≤ 5, using eight state-of-the-art cosmological hydrodynamics simulation codes in the AGORA project. In three of the participating codes, a distinct, rotation-dominated inner core emerges with a formation timescale of ≲300 Myr, largely driven by a major merger event, while two other codes exhibit similar signs of wet compaction—gaseous shrinkage into a compact starburst phase—at earlier epochs. The remaining three codes show only weak evidence of wet compaction. Consequently, we divide the simulated galaxies into two groups: those with strong compaction signatures and those with weaker ones. Galaxies in these two groups differ in size, stellar age gradients, and disk-to-total mass ratios. Specifically, codes with strong wet compaction build their outer disks in an inside-out fashion, leading to negative age gradients, whereas codes with weaker compaction feature flat or positive age gradients caused primarily by outward stellar migration. Although the stellar half-mass radii of these two groups diverge at z ∼ 3, the inclusion of dust extinction brings their sizes and shapes in mock observations closer to each other and to observed galaxies. We attribute the observed morphological differences primarily to variations in the stellar feedback implementations—such as delayed cooling timescales, and feedback strengths—that regulate both the onset and duration of compaction. Overall, our results suggest that disk assembly at high redshifts is highly sensitive to the details of the stellar feedback prescriptions in simulations.
Jung, Minyong [Seoul National Univ. (Korea, Republ↗
The short ionizing photon mean free path at z = 6 in Cosmic Dawn III, a new fully coupled radiation-hydrodynamical simulation of the Epoch of Reionization
ABSTRACT Recent determinations of the mean free path of ionizing photons (mfp) in the intergalactic medium (IGM) at z = 6 are lower than many theoretical predictions. In order to gain insight, we investigate the evolution of the mfp in our new massive fully coupled radiation-hydrodynamics cosmological simulation of reionization: Cosmic Dawn III (CoDa III). CoDa III’s scale ($\rm 94^3 \, cMpc^3$) and resolution ($\rm 8192^3$ grid) make it particularly suitable to study the IGM during reionization. The simulation was performed with ramses-cudaton on Summit, and used 13 1072 processors coupled to 24 576 GPUs, making it the largest reionization simulation, and largest ever ramses simulation. A superior agreement with global constraints on reionization is obtained in CoDa III over Cosmic Dawn II (CoDa II), especially for the evolution of the neutral hydrogen fraction and the cosmic photoionization rate, thanks to an improved calibration, later end of reionization (z = 5.6), and higher spatial resolution. Analysing the mfp, we find that CoDa III reproduces the most recent observations very well, from z = 6 to z = 4.6. We show that the distribution of the mfp in CoDa III is bimodal, with short (neutral) and long (ionized) mfp modes, due to the patchiness of reionization and the coexistence of neutral versus ionized regions during reionization. The neutral mode peaks at sub-kpc to kpc scales of mfp, while the ionized mode peak evolves from 0.1 Mpc h−1 at z = 7 to ∼10 Mpc h−1 at z = 5.2. Computing the mfp as the average of the ionized mode provides the best match to the recent observational determinations. The distribution reduces to a single neutral (ionized) mode at z > 13 (z < 5).
Lewis, Joseph S. W. (ORCID:0000000179178474)↗
Constraints on dark photon dark matter from Lyman- α forest simulations and an ultrahigh signal-to-noise quasar spectrum
The ultralight dark photon is a well-motivated, hypothetical dark matter candidate. In a dilute plasma, they can resonantly convert into photons, and heat up the intergalactic medium between galaxies. In this work, we explore the dark photon dark matter parameter space by comparing synthetic Lyman- α forest data from cosmological hydrodynamical simulations to observational data from VLT/UVES of the quasar HE0940-1050 ( z em = 3.09 ). We use a novel flux normalization technique that targets underdense gas, reshaping the flux probability distribution. Not only do we place robust constraints on the kinetic mixing parameter of dark photon dark matter, but notably our findings suggest that this model can still reconcile simulated and observed Doppler parameter distributions of z ∼ 0 Lyman- α lines, as seen by HST/COS. This work opens new pathways for the use of the Lyman- α forest to explore new physics, and can be extended to other scenarios such as primordial black hole evaporation, dark matter decay, and annihilation.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗