arXiv subjects

Mauro Rigo

Publications and source records attributed to Mauro Rigo.

3 recordsLinked to original sources

arXiv2026

Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps

Extracting maximum cosmological information from current and upcoming large-scale structure data requires going beyond summary statistics as currently used in likelihood-based inference. Simulation-Based Inference (SBI) promises to enable the exploitation of field-level information and the rich physics of modern hydrodynamical simulations. We develop a proof-of-concept SBI pipeline to explore its potential to constrain the cosmological parameters $\{\Omega_{\rm m}, \sigma_8\}$ from galaxy number counts, neutral hydrogen (HI) intensity mapping and their combination. We use neural emulators trained on full hydrodynamical simulations to generate galaxy and HI maps from fast, approximate dark matter simulations. Combined with neural posterior estimation, this enables the estimation of cosmological parameters while marginalizing over astrophysical effects. We perform inference both on the power spectrum and on representations derived from field-level 2D or 3D maps, comparing results from each probe and the combination of both tracers, and assessing the impact of data compression and multi-tracers information on cosmological constraints. Combining galaxy and HI fields improves constraints with respect to single-tracer cases by a factor 2 to 7 in terms of a Figure of Merit describing the joint precision on cosmological parameters, depending on the tracer/configuration. Moving from summary statistics to field-level inference leads to a consistent gain in constraining power of about a factor 3, with 3D maps providing the most precise and well-calibrated posteriors. This gain in precision is robust even when astrophysical parameters are marginalized over. Further developments (including realistic survey effects and improvements in emulators' faithfulness) will enable the application of this analysis pipeline to upcoming surveys.

astro-ph.CO↗

arXiv2025

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

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.

astro-ph.CO↗

arXiv2022

Solving the nuclear pairing model with neural network quantum states

We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.

nucl-th↗