Galaxy Phase-space and Field-level Cosmology: The Strength of Semianalytic Models

Abstract Semianalytic 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 3D 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 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 semianalytic models (GAEA, SC-SAM, and S hark ) and, more remarkably, to hydrodynamical simulations ( Astrid , S IMBA , 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 semianalytic 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.