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Learning the Universe Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours

CC Lovell, T Starkenburg, M Ho… - arXiv preprint arXiv …, 2024 - arxiv.org
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… the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference …

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@article{2411.13960v1,
Author        = {Christopher C. Lovell and Tjitske Starkenburg and Matthew Ho and Daniel Anglés-Alcázar and Romeel Davé and Austen Gabrielpillai and Kartheik Iyer and Alice E. Matthews and William J. Roper and Rachel Somerville and Laura Sommovigo and Francisco Villaescusa-Navarro},
Title         = {Learning the Universe: Cosmological and Astrophysical Parameter
  Inference with Galaxy Luminosity Functions and Colours},
Eprint        = {2411.13960v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.GA},
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--near infrared stellar emission from galaxies
in thousands of cosmological hydrodynamic simulations from the CAMELS suite,
including the Swift-EAGLE, Illustris-TNG, Simba & Astrid galaxy formation
models. For each galaxy we calculate the rest-frame luminosity in a number of
photometric bands, including the SDSS $\textit{ugriz}$ and GALEX FUV & NUV
filters; this dataset 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, and obtain constraints on both cosmological and astrophysical
parameters. Both colour distributions and luminosity functions provide
complementary information on certain parameters when performing inference. Most
interestingly we achieve constraints on $\sigma_8$, describing the clustering
of matter. This 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 generalises 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 at:
https://camels.readthedocs.io/ .},
Year          = {2024},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2411.13960v1},
File          = {2411.13960v1.pdf}
}

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