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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Numerical discreteness errors in multispecies cosmological N -body simulations

ABSTRACT We present a detailed analysis of numerical discreteness errors in two-species, gravity-only, cosmological simulations using the density power spectrum as a diagnostic probe. In a simple set-up where both species are initialized with the same total matter transfer function, biased growth of power forms on small scales when the solver force resolution is finer than the mean interparticle separation. The artificial bias is more severe when individual density and velocity transfer functions are applied. In particular, significant large-scale offsets in power are measured between simulations with conventional offset grid initial conditions when compared against converged high-resolution results where the force resolution scale is matched to the interparticle separation. These offsets persist even when the cosmology is chosen so that the two particle species have the same mass, indicating that the error is sourced from discreteness in the total matter field as opposed to unequal particle mass. We further investigate two mitigation strategies to address discreteness errors: the frozen potential method and softened interspecies short-range forces. The former evolves particles under the approximately ‘frozen’ total matter potential in linear theory at early times, while the latter filters cross-species gravitational interactions on small scales in low-density regions. By modelling closer to the continuum limit, both mitigation strategies demonstrate considerable reductions in large-scale power spectrum offsets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
DOE OSTI2022

Stringent σ8 constraints from small-scale galaxy clustering using a hybrid MCMC + emulator framework

ABSTRACT We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis. Our hybrid approach first samples the HOD space on a fixed cosmological simulation grid to constrain the high-likelihood region of cosmology + HOD parameter space, and then constructs the emulator within this constrained region. This approach significantly reduces the parameter volume emulated over, thus achieving much smaller emulator errors with fixed number of training points. We demonstrate that this combined with state-of-the-art simulations result in tight emulator errors comparable to expected DESI Y1 LRG sample variance. We leverage the new abacussummit simulations and apply our hybrid approach to CMASS non-linear galaxy clustering data. We infer constraints on σ8 = 0.762 ± 0.024 and fσ8(zeff = 0.52) = 0.444 ± 0.016, the tightest among contemporary galaxy clustering studies. We also demonstrate that our fσ8 constraint is robust against secondary biases and other HOD model choices, a critical first step towards showcasing the robust cosmology information accessible in non-linear scales. We speculate that the additional statistical power of DESI Y1 should tighten the growth rate constraints by at least another 50–60 ${{\ \rm per\ cent}}$, significantly elucidating any potential tension with Planck. We also address the ‘lensing is low’ tension, which we find to be in the same direction as a potential tension in fσ8. We show that the combined effect of a lower fσ8 and environment-based bias accounts for approximately $50{{\ \rm per\ cent}}$ of the discrepancy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Deep learning insights into cosmological structure formation

The evolution of linear initial conditions present in the early Universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation. Published by the American Physical Society 2024

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Signatures of self-interacting dark matter on cluster density profile and subhalo distributions

Non-gravitational interactions between dark matter particles with strong scattering, but relatively small annihilation and dissipation, has been proposed to match various observables on cluster and group scales. In this paper, we present the results from large cosmological simulations which include the effects of different self-interaction scenarios. In particular we explore a model with the differential cross section that can depend on both the relative velocity of the interacting particles and the angle of scattering. We focus on how quantities, such as the stacked density profiles, subhalo counts and the splashback radius change as a function of different forms of self-interaction. We find that self-interactions not only affect the central region of the cluster, the effect well known from previous studies, but also significantly alter the distribution of subhalos and the density of particles out to the splashback radius. Therefore, our results suggest that current weak lensing data can already put constraints on the self-interaction cross-section that are only slightly weaker than the Bullet Cluster constraints (σ/m ≲ 2 cm 2 /g), and future lensing surveys should be able to tighten them even further making halo profiles on cluster scales a competitive probe for DM physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Association between optically identified galaxy clusters and the underlying dark matter halos

Clusters of galaxies trace massive dark matter halos in the Universe, but they can include multiple halos projected along lines of sight. Here, we study the halos contributing to clusters using the Cardinal simulation, which mimics the Dark Energy Survey data. We use the red-sequence-based cluster finding algorithm redMaPPer as a case study. For each cluster, we identify the halos hosting its member galaxies, and we define the main halo as the one contributing the most to the cluster's richness ($λ$, the estimated number of member galaxies). At $z=0.3$, for clusters with $λ> 60$, the main halo typically contributes to $92\%$ of the richness, and this fraction drops to $67\%$ for $λ\approx 20$. Defining "clean" clusters as those with $\geq50\%$ of the richness contributed by the main halo, we find that $100\%$ of the $λ> 60$ clusters are clean, while $73\%$ of the $λ\approx 20$ clusters are clean. Three halos can usually account for more than $80\%$ of the richness of a cluster. The main halos associated with redMaPPer clusters have a completeness ranging from $98\%$ at virial mass $10^{14.6}~h^{-1}M_{\odot}$ to $64\%$ at $10^{14}~h^{-1}M_{\odot}$. In addition, we compare the inferred cluster centers with true halo centers, finding that $30\%$ of the clusters are miscentered with a mean offset $40\%$ of the cluster radii, in agreement with recent X-ray studies. These systematics worsen as redshift increases, but we expect that upcoming surveys extending to longer wavelengths will improve the cluster finding at high redshifts. Our results affirm the robustness of the redMaPPer algorithm and provide a framework for benchmarking other cluster-finding strategies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bridging Scales in Black Hole Accretion and Feedback: Relativistic Jet Linking the Horizon to the Host Galaxy

Simulating black hole (BH) accretion and feedback from the BH horizon to galactic scales is extremely challenging, as it involves a vast range of scales. Recently, our multizone method has successfully achieved global dynamical steady states of hot accretion flows in 3D general relativistic magnetohydrodynamic simulations by tracking the bidirectional interaction between a nonspinning BH and its host galaxy. In this paper, we present technical improvements to the method and apply it to spin a * = 0.9 BHs, which power relativistic jets. We first test the new multizone setup with a smaller Bondi radius, R B ≈ 400 r g , where r g is the gravitational radius. The strongly magnetized accretion launches a relativistic jet with an intermediate feedback efficiency η ∼ 30%, in between that of a prograde (η ∼ 100%) and retrograde (η ∼ 10%) torus. Interestingly, both prograde and retrograde simulations also eventually converge to the same intermediate efficiency when evolved long enough, as accumulated magnetic fields remove gas rotation. We then extend strongly magnetized simulations to larger Bondi radii, R B ≈ 2 × 10 3 , 2 × 10 4 , 2 × 10 5 r g . We find that the BH accretion rate $\dot{M}$ is suppressed with respect to the Bondi rate as $\dot{M}_{\textrm{B}}$ as $\dot{M}/\dot{M}_{\textrm{B}} ∝ R_{\textrm{B}}^{-1/2}$. However, despite some variability, the time-averaged feedback efficiency remains at η ∼ 30%, independent of R B . This suggests that BH feedback efficiency in hot accretion flows is mainly governed by the BH spin (a * ) rather than by the galactic properties (R B ). From these first-principles simulations, we provide a feedback subgrid prescription for cosmological simulations: $\dot{E}_{\textrm{fb}} = 2$ x $10^{-3}[R_{\textrm{B}}/(2$ x $10^5 r_g)]^{-1/2}$ $\dot{M}_{\textrm{B}}c^2$ for BH spin a * = 0.9.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Good and proper: self-similarity of N -body simulations with proper force softening

ABSTRACT Analysis of self-similarity in scale-free N-body simulations reveals the spatial and temporal scales for which statistics measured in cosmological simulations are converged to the physical continuum limit. We examine how the range of scales in which the two-point correlation function is converged depends on the force softening length and whether it is held constant in comoving or proper coordinates. We find that a proper softening that reaches roughly 1/30th of the inter-particle spacing by the end of the simulation resolves the same spatial and temporal scales as a comoving softening of the same length while using a third fewer time-steps, for a range of scale factors typical to Lambda cold dark matter (ΛCDM) simulations. We additionally infer an inherent resolution limit, set by the particle mass and scaling as a−1/2, beyond which reducing the softening does not improve the resolution. We postulate a mapping of these results with spectral index n = −2 to ΛCDM simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

New Constraints on the Dark Matter Density Profiles of Dwarf Galaxies from Proper Motions of Globular Cluster Streams

The central density profiles in dwarf galaxy halos depend strongly on the nature of dark matter (DM). Recently, in Malhan et al. we employed N-body simulations to show that the cuspy cold DM subhalos predicted by cosmological simulations can be differentiated from cored subhalos using the properties of accreted globular cluster (GC) streams since these GCs experience tidal stripping within their parent halos prior to accretion onto the Milky Way. We previously found that clusters that are accreted within cuspy subhalos produce streams with larger physical widths and higher dispersions in line-of-sight velocity and angular momentum than streams that are accreted within cored subhalos. Here, we use the same suite of simulations to demonstrate that the dispersion in the tangential velocities of streams (${\sigma }_{{v}_{\mathrm{Tan}}}$) is also sensitive to the central DM density profiles of their parent dwarfs and GCs that they were accreted from; cuspy subhalos produce streams with larger ${\sigma }_{{v}_{\mathrm{Tan}}}$ than those accreted inside cored subhalos. Using Gaia EDR3 observations of multiple GC streams we compare their ${\sigma }_{{v}_{\mathrm{Tan}}}$ values with simulations. The measured ${\sigma }_{{v}_{\mathrm{Tan}}}$ values are consistent with both an “in situ” origin and with accretion inside cored subhalos of M ~ 10 8–9 M ⊙ (or very low-mass cuspy subhalos of mass ~10 8 M ⊙ ). Despite the large current uncertainties in ${\sigma }_{{v}_{\mathrm{Tan}}}$, we find a low probability that any of the progenitor GCs were accreted from cuspy subhalos of M ≳ 10 9 M ⊙ . The uncertainties on Gaia tangential velocity measurements are expected to decrease in future and will allow for stronger constraints on subhalo DM density profiles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

UniverseMachine: Predicting Galaxy Star Formation over Seven Decades of Halo Mass with Zoom-in Simulations

In this work, we apply the empirical galaxy–halo connection model UniverseMachine to dark-matter-only zoom-in simulations of isolated Milky Way (MW)–mass halos, along with their parent cosmological simulations. This application extends UniverseMachine predictions into the ultrafaint dwarf galaxy regime (10 2 M ⊙ ≤ M * ≤ 10 5 M ⊙) and yields a well-resolved stellar mass–halo mass (SMHM) relation over the peak halo mass range of 10 8 –10 15 M ⊙. The extensive dynamic range provided by the zoom-in simulations allows us to assess specific aspects of dwarf galaxy evolution predicted by UniverseMachine. In particular, although UniverseMachine is not constrained for dwarf galaxies with M * ≲ 10 8 M ⊙, our predicted SMHM relation is consistent with that inferred for MW satellite galaxies at z = 0 using abundance matching. However, UniverseMachine predicts that nearly all galaxies are actively star-forming below M * ~ 10 7 M ⊙ and that these systems typically form more than half of their stars at z ≲ 4, which is discrepant with the star formation histories of Local Group dwarf galaxies that favor early quenching.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Indra: a public computationally accessible suite of cosmological N -body simulations

ABSTRACT Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogues, and 505 time-steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large data sets public and computationally accessible.

Falck, Bridget↗
DOE OSTI2025

Enhanced Star Formation and Black Hole Accretion Rates in Galaxy Mergers in IllustrisTNG50

Many theoretical and observational studies have suggested that galaxy mergers may trigger enhanced star formation or active galactic nucleus activity. We present an analysis of merging and nonmerging galaxies from 0.2 ≤ z ≤ 3 in the IllustrisTNG50 simulation. These galaxies encompass a range of masses (M ⋆ > 10 8 M ⊙ ) and mass ratios (≥1:10) and multiple merger stages. We examine the effect that galaxy mergers have on star formation and black hole accretion rates in the TNG50 universe. We additionally investigate how galaxy and black hole mass, merger stage, merger mass ratio, and redshift affect these quantities. Mergers in our sample show excess specific star formation rates (sSFR) at z ≤ 3 and enhanced specific black hole accretion rates (sBHAR) at z ≲ 2. The difference between sSFRs and sBHARs in the merging sample compared to the nonmerging sample increases as redshift decreases. Additionally, we show that these enhancements persist for at least ∼1 Gyr after the merger event. Investigating how mergers behave in the TNG50 simulation throughout cosmic time enables both a better appreciation of the importance of spatial resolution in cosmological simulations and a better basis to understand our high- z Universe with observations from JWST.

AGN host galaxies↗
DOE OSTI2023

Interacting galaxies in the IllustrisTNG simulations – V. Comparing the influence of star-forming versus passive companions

ABSTRACT We study interacting galaxy pairs in the TNG100-1 and TNG300-1 cosmological simulations using previously generated closest companion samples. We study the specific star-formation rates (sSFRs) of massive ($10^{10} \, {\rm M}_{\odot } \lt M_* \lt 10^{12} \, {\rm M}_{\odot }$ ) galaxies at z ≤ 0.2 as a function of separation from the closest companion galaxy. We split our sample based on whether the companion galaxy is star-forming or passive. We find that galaxies with close star-forming companions have sSFRs that are enhanced (on average) by a factor of 2.9 ± 0.3 in TNG100-1 and 2.27 ± 0.06 in TNG300-1 compared to controls, with enhancements present out to separations of ∼300 kpc. Galaxies with passive companions in TNG300-1 exhibit mild sSFR suppression (∼12 per cent) at 100–300 kpc and small sSFR enhancements at separations below 50 kpc. sSFR suppression is strongest in pairs where the galaxy’s stellar mass is more than 2 times that of its passive companion. By generating a stellar mass-matched (‘twinned’) sample in TNG300-1, we show that differences in sSFR trends between companion types are not a result of intrinsic stellar mass differences in star-forming versus passive galaxies. We compare with an analogous sample of galaxy pairs from SDSS, finding consistent results between observations and simulations. Overall, we find that star-forming galaxies show enhanced sSFRs regardless of companion type, but that galaxies with close passive companions are more likely to be passive themselves.

Astronomy & Astrophysics↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Joint Modeling of GD-1 and C-19 as Old Streams

DESI observational data for the GD-1 and C-19 streams are compared to stream simulations in a common evolving multi-halo potential of a Milky Way-like galaxy based on a cosmological simulation. The goal is to find the best match of the stream velocity spread and the density power spectrum stream density to simulations having either CDM or WDM subhalos. The cocoon velocity width integrated over the length of the stream is independent of orbital blurring along the stream and the power spectrum integrates over the width of the stream, sidestepping the geometric details of the streams. Streams develop from star clusters inserted at $\simeq$1 Gyr after the Big Bang and evolved for 13 Gyr to their current orbital positions. Streams in a CDM subhalo population provide the best match to the velocity width, with streams younger than 10 Gyr ruled out as insufficiently hot. The progenitor star cluster masses, which determine the fraction of stars released at late times which comprise the stream core, are found to be $\simeq 8\times 10^4 M_\odot$ for GD-1 and $\simeq 4\times 10^4 M_\odot$ for C-19, although the mass depends on the star cluster half mass radius. Stream heating leads to stream lumpiness which is measurable for the relatively large and clean GD-1 dataset. The stream density power spectrum measured along the length of the DESI GD-1 sample is in good agreement with CDM simulations, with 1.7 to 1.9 times more power than WDM 7 keV and 5.5 keV simulations.

Carlberg, Raymond G. [Toronto U.] (ORCID:000000027↗
DOE OSTI2021

On the quenching of star formation in observed and simulated central galaxies: evidence for the role of integrated AGN feedback

ABSTRACT In this paper, we investigate how massive central galaxies cease their star formation by comparing theoretical predictions from cosmological simulations: EAGLE, Illustris, and IllustrisTNG with observations of the local Universe from the Sloan Digital Sky Survey (SDSS). Our machine learning (ML) classification reveals supermassive black hole mass (MBH) as the most predictive parameter in determining whether a galaxy is star forming or quenched at redshift z = 0 in all three simulations. This predicted consequence of active galactic nucleus (AGN) quenching is reflected in the observations, where it is true for a range of indirect estimates of MBH via proxies as well as its dynamical measurements. Our partial correlation analysis shows that other galactic parameters lose their strong association with quiescence, once their correlations with MBH are accounted for. In simulations, we demonstrate that it is the integrated power output of the AGN, rather than its instantaneous activity, which causes galaxies to quench. Finally, we analyse the change in molecular gas content of galaxies from star-forming to passive populations. We find that both gas fractions (fgas) and star formation efficiencies (SFEs) decrease upon transition to quiescence in the observations but SFE is more predictive than fgas in the ML passive/star-forming classification. These trends in the SDSS are most closely recovered in IllustrisTNG and are in direct contrast with the predictions made by Illustris. We conclude that a viable AGN feedback prescription can be achieved by a combination of preventative feedback and turbulence injection which together quench star formation in central galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗