May 13, 2024, 4:43 a.m. | Natal\'i S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, Helen Shao, Lucia A. Perez, Tiago Castro, Yueying Ni, Christopher C. Lovell, E

cs.LG updates on

arXiv:2310.15234v2 Announce Type: replace-cross
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 $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations …

abstract arxiv cs.lg effects free galaxy graph graph neural networks impact inference likelihood networks neural networks parameters redshift replace scale simulation surveys train type

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