  Using a large sample of galaxies taken from the Cosmology and Astrophysics
with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic
simulations varying both cosmological and astrophysical parameters, we train a
normalizing flow (NF) to map the probability of various galaxy and halo
properties conditioned on astrophysical and cosmological parameters. By
leveraging the learnt conditional relationships we can explore a wide range of
interesting questions, whilst enabling simple marginalisation over nuisance
parameters. We demonstrate how the model can be used as a generative model for
arbitrary values of our conditional parameters; we generate halo masses and
matched galaxy properties, and produce realisations of the halo mass function
as well as a number of galaxy scaling relations and distribution functions. The
model represents a unique and flexible approach to modelling the galaxy-halo
relationship.
