{"ID":2830059,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10222","arxiv_id":"2512.10222","title":"Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models","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, $Ω_{\\rm m}$, 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.","short_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...","url_abs":"https://arxiv.org/abs/2512.10222","url_pdf":"https://arxiv.org/pdf/2512.10222v1","authors":"[\"Natalí S. M. de Santi\",\"Francisco Villaescusa-Navarro\",\"Pablo Araya-Araya\",\"Gabriella De Lucia\",\"Fabio Fontanot\",\"Lucia A. Perez\",\"Manuel Arnés-Curto\",\"Violeta Gonzalez-Perez\",\"Ángel Chandro-Gómez\",\"Rachel S. Somerville\",\"Tiago Castro\"]","published":"2025-12-11T02:13:38Z","proceeding":"astro-ph.CO","tasks":"[\"astro-ph.CO\",\"astro-ph.GA\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}