JERALD: high-fidelity dark matter, stellar mass, and neutral hydrogen maps from fast N-body simulations
File(s)
Published version
Author(s)
Rigo, Mauro
Trotta, Roberto
Viel, Matteo
Type
Journal Article
Abstract
We present a new code and approach, JERALD – JAX Enhanced Resolution Approximate Lagrangian Dynamics –, that improves on and extends the Lagrangian Deep Learning method of Dai & Seljak (2021), producing high-resolution dark matter, stellar mass and neutral hydrogen maps from lower-resolution approximate N-body simulations. The model is trained using the Sherwood-Relics simulation suite (for a fixed cosmology), specifically designed for the intergalactic medium and the neutral hydrogen distribution in the cosmic web. The output is tested in the redshift range from z = 5 to z = 0 and the generalization properties of the learned mapping is demonstrated. JERALD produces maps with dark matter, stellar and neutral hydrogen power spectra in excellent agreement with full-hydrodynamical simulations with 8× higher resolution, at large and intermediate scales; in particular, JERALD’s neutral hydrogen power spectra agree with their higher-resolution full-hydrodynamical counterparts within 90 per cent up to k 1 h Mpc−1 and within 70 per cent up to k 10 h Mpc¯¹. JERALD provides a fast, accurate, and physically motivated approach that we plan to embed in a statistical inference pipeline, such as Simulation-Based Inference, to constrain dark matter properties from large- to intermediate-scale structure observables.
Date Issued
2025-07-01
Date Acceptance
2025-05-29
Citation
Monthly Notices of the Royal Astronomical Society, 2025, 541 (1), pp.166-178
ISSN
0035-8711
Publisher
Oxford University Press
Start Page
166
End Page
178
Journal / Book Title
Monthly Notices of the Royal Astronomical Society
Volume
541
Issue
1
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Subjects
(cosmology:) large-scale structure of Universe
(galaxies:) intergalactic medium
Astronomy & Astrophysics
CONSTRAINTS
EVOLUTION
galaxies: formation
GALAXY FORMATION
HALO MASS
methods: numerical
Physical Sciences
Science & Technology
software: machine learning
Publication Status
Published
Date Publish Online
2025-06-12