Cosmological Inference from Galaxy Populations with Machine Learning

          @misc{ scivideos_PIRSA:26060041,
            doi = {10.48660/26060041},
            url = {https://pirsa.org/26060041},
            author = {},
            keywords = {Cosmology},
            language = {en},
            title = {Cosmological Inference from Galaxy Populations with Machine Learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {jun},
            note = {PIRSA:26060041 see, \url{https://scivideos.org/pirsa/26060041}}
          }
          

Abstract

Galaxies are the primary tracers of the large-scale structure of the Universe and are traditionally used through summary statistics such as correlation functions and power spectra to constrain cosmological models. However, galaxy populations themselves contain rich information through their spatial distribution, environments, and internal properties, potentially extending beyond the information captured by standard summary statistics. In this talk, I will present a series of machine learning approaches designed to extract cosmological information directly from galaxy catalogs. I will discuss work using graph neural networks (GNNs) to infer cosmological parameters from simulated galaxy populations, including studies demonstrating robustness to observational effects and domain shifts across semi-analytic and hydrodynamical galaxy formation models. I will also present recent work exploring the cosmological information content of individual galaxies using symbolic regression, as well as ongoing efforts based on probabilistic generative models. Together, these results suggest that galaxy populations may encode cosmological information in ways that complement traditional large-scale structure analyses, opening new avenues for field-level and simulation-based inference in upcoming surveys