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 2000 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 allows our models to infer the value of ωm, at fixed ωb, with a ∼10% precision, while no constraint can be placed on σ 8. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, z ≤ 3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of ωm. We believe that our results can be explained by considering that changes in the value of ωm, or potentially ωb/ωm, affect the dark matter content of galaxies, which leaves a signature in galaxy properties distinct from the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science