{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/higlow-conditional-normalizing-flows-for-high","title":"HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling","arxiv_id":"2211.12724","date":"2022-11-23","proceeding":null,"authors":["Roy Friedman","Sultan Hassan"],"abstract":"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. 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