HInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks.

Upcoming 21 cm surveys will map the spatial distribution of cosmic neutral hydrogen (H i) over very large cosmological volumes. In order to maximize the scientific return of these surveys, accurate theoretical predictions are needed. Hydrodynamic simulations currently are the most accurate tool to provide those predictions in the mildly to nonlinear regime. Unfortunately, their computational cost is very high: tens of millions of CPU hours. We use convolutional neural networks to find the mapping between the spatial distribution of matter from N-body simulations and H i from the state-of-the-art hydrodynamic simulation IllustrisTNG. Our model performs better than the widely used theoretical model: halo occupation distribution for all statistical properties up to the nonlinear scales k ≲ 1 hr Mpc⁻¹. Our method allows the generation of 21 cm mocks over very big cosmological volumes with similar properties to hydrodynamic simulations. [ABSTRACT FROM AUTHOR]