How DREAMS Are Made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds
Year: 2026
Type: article
Abstract: 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 semianalytic models to hydrodynamics. 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 the 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.
Source: The Astrophysical Journal
Authors: Tri Nguyen, Francisco Villaescusa-Navarro, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Paul Torrey, Arya Farahi, Alex M. Garcia, Jonah C. Rose, Stephanie O’Neil, Mark Vogelsberger, Xuejian Shen, Cian Roche, Daniel Anglés-Alcázar, Nitya Kallivayalil, Julian B. Muñoz, Francis-Yan Cyr-Racine, Sandip Roy, Lina Necib, Kassidy E. Kollmann
Institutions: Northwestern University, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, Princeton University, Flatiron Institute, Harvard University, Center for Astrophysics Harvard & Smithsonian, University of Virginia, The University of Texas at Austin, University of Florida, University of Connecticut, University of New Mexico
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
Awards: CAREER: Building the Merger Tree of the Milky Way with Machine Learning, G1441931114, G2182738384, G2366492356, NSF-BSF: Dark-Matter Interactions at Cosmic Dawn, G2595738051, RII Track-4:NSF: Probing the Dark Sector through Telescopes, THEORETICAL HIGH ENERGY PHYSICS, Graduate Research Fellowship Program (GRFP), AI Institute: AI Research Institute for Fundamental Interactions, G3682255939, G397603045, Collaborative Research: CDS&E: Systematic Predictions for Dynamical Signatures of New Dark Matter Physics in Galaxies, G5082610212, G546066981, G6027933290, THEORETICAL HIGH ENERGY PHYSICS, G638484911, G648414855, G6671297155, G6794394390, Collaborative Research: Discriminating Between Galactic Feedback Models with Next Generation Observations, G7262085928, G7372984379, Collaborative Research: CDS&E: Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to maximize the science return of next generation cosmological experiments, Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics, Decoding the First Galaxies via 21-cm Fluctuations, NeTS: Small: Design and Initialization of Secure Wireless Networks: Foundations and Practice, G97843138
OpenAlex ID: W7126202061