Learning the universe: cosmological and astrophysical parameter inference with galaxy luminosity functions and colours

Abstract

We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation-based inference approach. Using the synthesizer code, we simulate the dust attenuated ultraviolet (UV)–near-infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations from the CAMELS suite, including the swift-eagle, ILLUSTRISTNG, simba, and astrid galaxy formation models. For each galaxy, we calculate the rest-frame luminosity in a number of photometric bands, including the SDSS ugriz and GALEX far- and near-UV filters; this data set represents the largest catalogue of synthetic photometry based on hydrodynamic galaxy formation simulations produced to date, totalling > 200 million sources. From these, we compile luminosity functions and colour distributions, and find clear dependencies on both cosmology and feedback. We then perform simulation-based (likelihood-free) inference using these distributions to obtain constraints on Ωm, σ8, and four parameters controlling the strength of stellar and active galactic nucleus feedback. Both colour distributions and luminosity functions provide complementary information on certain parameters when performing inference. We achieve constraints on the stellar feedback parameters, as well as Ωm and σ8. The latter is attributable to the fact that the photometry encodes the star formation–metal enrichment history of each galaxy; galaxies in a universe with a higher σ8 tend to form earlier and have higher metallicities, which leads to redder colours. We find that a model trained on one galaxy formation simulation generalizes poorly when applied to another, and attribute this to differences in the subgrid prescriptions, and lack of flexibility in our emission modelling. The photometric catalogues are publicly available.