Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Published in arxiv, 2025
NatalĂ S. M. de Santi et al 2025 arXiv https://arxiv.org/abs/2512.10222
Paper highlights:
- Trains a graph neural network combined with a moment neural network on semi-analytic galaxy catalogs to infer the matter density parameter Ωm.
- Achieves ~10% precision using only galaxy positions and velocities, with strong extrapolation from L-Galaxies to other SAMs and to full hydrodynamical simulations.
- Demonstrates robustness to astrophysical modeling, subgrid physics, and halo-profile prescriptions, highlighting the value of SAMs for cosmological inference.
You can read the paper here.