HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows

Understand

A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys.

• It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information.
• We present HIFlow: a fast generative model of the neutral hydrogen (HI) maps that is conditioned only on cosmology ($\Omega_{m}$ and $\sigma_{8}$) and designed using a class of normalizing flow models, the Masked Autoregressive Flow (MAF).
• HIFlow is trained on the state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project.