Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

TL;DR

This paper introduces a machine learning model using galaxy positions and velocities to accurately estimate cosmological parameters across various galaxy simulations, demonstrating robustness and potential for cosmological inference.

Contribution

The authors develop a graph neural network coupled with a moment neural network that estimates matter density parameters from galaxy phase-space data, effective across multiple semi-analytic and hydrodynamical models.

Findings

Achieves ~10% precision in estimating $ m extbf{ extit{ extOmega}}_{m}$.

Successfully extrapolates predictions to different models and simulations.

Robust to variations in astrophysical and subgrid physics.

Abstract

Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating accurate galaxy catalogs, offering a faster and less computationally expensive option compared to full hydrodynamical simulations. In this paper, we demonstrate that using only galaxy $3$D positions and radial velocities, we can train a graph neural network coupled to a moment neural network to obtain a robust machine learning based model capable of estimating the matter density parameters, $\Omega_{\rm m}$, with a precision of approximately 10%. The network is trained on ($25 h^{-1}$Mpc)$^3$ volumes of galaxy catalogs from L-Galaxies and can successfully extrapolate its predictions to other semi-analytic models (GAEA, SC-SAM, and Shark) and, more remarkably, to hydrodynamical simulations (Astrid, SIMBA, IllustrisTNG, and SWIFT-EAGLE). Our results show that the network is robust to variations in astrophysical and subgrid physics, cosmological and astrophysical parameters, and the different halo-profile treatments used across simulations. This suggests that the physical relationships encoded in the phase-space of semi-analytic models are largely independent of their specific physical prescriptions, reinforcing their potential as tools for the generation of realistic mock catalogs for cosmological parameter inference.