Cosmology with multiple galaxies

Understand

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.