Cosmology with one galaxy?
Francisco Villaescusa-Navarro, Jupiter Ding, Shy Genel, Stephanie, Tonnesen, Valentina La Torre, David N. Spergel, Romain Teyssier, Yin Li,, Caroline Heneka, Pablo Lemos, Daniel Angl\'es-Alc\'azar, Daisuke Nagai, Mark, Vogelsberger

TL;DR
This study demonstrates that the internal properties of individual galaxies, such as stellar mass and metallicity, can be used to infer key cosmological parameters like $\
Contribution
It introduces a neural network approach trained on hydrodynamic simulations to extract cosmological information from galaxy properties, highlighting the potential of galaxy data for cosmology.
Findings
Galaxy properties can constrain $\
Models are sensitive to subgrid physics variations.
Stellar mass, metallicity, and circular velocity are key indicators.
Abstract
Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2,000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allow our models to infer the value of , at fixed , with a precision, while no constraint can be placed on . Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all…
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