Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Published in JCAP, 2025
de Santi, N. S. M., Villaescusa-Navarro, F., Raul Abramo, L., et al. 2025, JCAP, 2025, 1, 082. doi:10.1088/1475-7516/2025/01/082 https://arxiv.org/abs/2310.15234](https://arxiv.org/abs/2310.15234
Abstract:
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of Ωm from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models.
You can read the paper here.