HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling by Roy Friedman et al. on Thursday 24 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.12724v1