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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.10222 |
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| _version_ | 1866914194037669888 |
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| author | de Santi, Natalí S. M. Villaescusa-Navarro, Francisco Araya-Araya, Pablo De Lucia, Gabriella Fontanot, Fabio Perez, Lucia A. Arnés-Curto, Manuel Gonzalez-Perez, Violeta Chandro-Gómez, Ángel Somerville, Rachel S. Castro, Tiago |
| author_facet | de Santi, Natalí S. M. Villaescusa-Navarro, Francisco Araya-Araya, Pablo De Lucia, Gabriella Fontanot, Fabio Perez, Lucia A. Arnés-Curto, Manuel Gonzalez-Perez, Violeta Chandro-Gómez, Ángel Somerville, Rachel S. Castro, Tiago |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models de Santi, Natalí S. M. Villaescusa-Navarro, Francisco Araya-Araya, Pablo De Lucia, Gabriella Fontanot, Fabio Perez, Lucia A. Arnés-Curto, Manuel Gonzalez-Perez, Violeta Chandro-Gómez, Ángel Somerville, Rachel S. Castro, Tiago Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Machine Learning 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. |
| title | Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Machine Learning |
| url | https://arxiv.org/abs/2512.10222 |