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

Дата публикации: 18-08-2026 00:00:00

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, Ωm, 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 Shark) and, more remarkably, to hydrodynamical simulations (Astrid, SIMBA, IllustrisTNG, and SWIFT-EAGLE). Our results show that the network...

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