JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen maps from fast N-body simulations

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 $k\simeq 1h$Mpc${}^{-1}$ and within 70% up to $k\simeq 10h$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.

12 pages, 10 figures, 1 table

References in corpus (11)

• The DESI Experiment Part I: Science,Targeting, and Survey Design
• A primer on hierarchical galaxy formation: the semi-analytical approach
• Fast likelihood-free cosmology with neural density estimators and active learning
• Unveiling Dark Matter free-streaming at the smallest scales with high redshift Lyman-alpha forest
• The CAMELS project: public data release
• The Sherwood-Relics simulations: overview and impact of patchy reionization and pressure smoothing on the intergalactic medium
• The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
• Bayesian forward modelling of cosmic shear data
• From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with Deep Learning
• The MillenniumTNG Project: Semi-analytic galaxy formation models on the past lightcone
• Painting baryons onto N-body simulations of galaxy clusters with image-to-image deep learning

Cited by in corpus (1)

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