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

HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling