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 $\Omega _{\mathrm{m}}$, $\sigma _{8}$, 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 $\Omega _{\mathrm{m}}$ and $\sigma _{8}$. 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 $\sigma _{8}$ 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.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-10-31
DOI
https://doi.org/10.1093/mnras/staf1888
Citations
2
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
2.31
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article

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

Francisco Villaescusa-Navarro, Daniel Anglés‐Alcázar, Romeel Davé, Matthew Ho et al.
2 citations
Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, Phenomena
2.31
article

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

Francisco Villaescusa-Navarro, Daniel Anglés‐Alcázar, Romeel Davé, Matthew Ho, Austen Gabrielpillai, Rachel S. Somerville, Tjitske Starkenburg, Kartheik G. Iyer, Christopher C. Lovell, William J Roper, A. M. Matthews, Laura Sommovigo
article en
2 citations

Abstract

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 $\Omega _{\mathrm{m}}$, $\sigma _{8}$, 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 $\Omega _{\mathrm{m}}$ and $\sigma _{8}$. 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 $\sigma _{8}$ 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.

Monthly Notices of the Royal Astronomical SocietyVol. 544(4)
Northwestern University (US), The Graduate Center, CUNY (US), University of Connecticut (US), University of Sussex (GB), City University of New York (US), Princeton University (US), Chestnut Hill College (US), University of Cambridge (GB), Royal Observatory (GB), South African Radio Astronomy Observatory (ZA), Institute of Astronomy (RU), Flatiron Health (United States) (US), Flatiron Institute (US), University College London (GB), University of Portsmouth (GB), Columbia University (US), University of the Western Cape (ZA), University of Edinburgh (GB)
Openalex Percentile: Top 10%
Galaxies: Formation, Evolution, Phenomena
2.31
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