Normalizing flows for random fields in cosmology
  Normalizing flows are a powerful tool to create flexible probability
distributions with a wide range of potential applications in cosmology. Here we
are studying normalizing flows which represent cosmological observables at
field level, rather than at the level of summary statistics such as the power
spectrum. We evaluate the performance of different normalizing flows for both
density estimation and sampling of near-Gaussian random fields, and check the
quality of samples with different statistics such as power spectrum and
bispectrum estimators. We explore aspects of these flows that are specific to
cosmology, such as flowing from a physical prior distribution and evaluating
the density estimation results in the analytically tractable correlated
Gaussian case.
