  Extracting the maximum amount of cosmological and astrophysical information
from upcoming large-scale surveys remains a challenge. This includes evaluating
the exact likelihood, parameter inference and generating new diverse synthetic
examples of the incoming high-dimensional data sets. In this work, we propose
the use of normalizing flows as a generative model of the neutral hydrogen (HI)
maps from the CAMELS project. Normalizing flows have been very successful at
parameter inference and generating new, realistic examples. Our model utilizes
the spatial structure of the HI maps in order to faithfully follow the
statistics of the data, allowing for high-fidelity sample generation and
efficient parameter inference.
