BibTeX

@article{2501.09168v1,
Author        = {Mauro Rigo and Roberto Trotta and Matteo Viel},
Title         = {JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen
  maps from fast N-body simulations},
Eprint        = {2501.09168v1},
DOI           = {10.1093/mnras/staf948},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
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-hydrodynamic simulations with
$8\times$ higher resolution, at large and intermediate scales; in particular,
JERALD's neutral hydrogen power spectra agree with their higher-resolution
full-hydrodynamic counterparts within 90% up to $k\simeq1\,h$Mpc$^{-1}$ and
within 70% up to $k\simeq10\,h$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.},
Year          = {2025},
Month         = {Jan},
Note          = {MNRAS, 541, 1 (2025), 166-178},
Url           = {http://arxiv.org/abs/2501.09168v1},
File          = {2501.09168v1.pdf}
}