Cosmology with one galaxy?
  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
$\Omega_{\rm m}$, at fixed $\Omega_{\rm b}$, with a $\sim10\%$ precision, while
no constraint can be placed on $\sigma_8$. Our results hold for any type of
galaxy, central or satellite, massive or dwarf, at all considered redshifts,
$z\leq3$, 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 $\Omega_{\rm m}$. We believe that our results can be explained
taking into account that changes in the value of $\Omega_{\rm m}$, or
potentially $\Omega_{\rm b}/\Omega_{\rm m}$, affect the dark matter content of
galaxies. That effect leaves a distinct signature in galaxy properties to 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.
