Cosmology with multiple galaxies

TL;DR

This study demonstrates that using multiple galaxy properties simultaneously improves constraints on cosmological parameters like _{ m m} and _{ m 8} from hydrodynamic simulations, enhancing precision and robustness.

Contribution

It introduces a neural network-based likelihood-free inference method that leverages multiple galaxies to better constrain cosmological and astrophysical parameters.

Findings

Using multiple galaxies increases _{ m m} constraint precision.

Multiple galaxies enable inference of previously poorly constrained parameters.

Model performance depends on the range of galaxy formation models used for training.

Abstract

Recent works have discovered a relatively tight correlation between $\Omega_{\rm m}$ and properties of individual simulated galaxies. Because of this, it has been shown that constraints on $\Omega_{\rm m}$ can be placed using the properties of individual galaxies while accounting for uncertainties on astrophysical processes such as feedback from supernova and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters ($\Omega_{\rm m}$ and $\sigma_8$) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the $\Omega_{\rm m}$ inference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on $\Omega_{\rm m}$ from one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models.