HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles The Astrophysical Journal Year : 2022

HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows

Sultan Hassan
  • Function : Author
Francisco Villaescusa-Navarro
  • Function : Author
David N. Spergel
  • Function : Author
Daniel Anglés-Alcázar
  • Function : Author
Shy Genel
  • Function : Author
Miles Cranmer
  • Function : Author
Greg L. Bryan
  • Function : Author
Romeel Davé
  • Function : Author
Rachel S. Somerville
  • Function : Author
Michael Eickenberg
  • Function : Author
Desika Narayanan
  • Function : Author
Shirley Ho
  • Function : Author
Sambatra Andrianomena
  • Function : Author

Abstract

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 (Ω m and σ 8) and designed using a class of normalizing flow models, the masked autoregressive flow. HIFLOW is trained on the state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. HIFLOW has the ability to generate realistic diverse maps without explicitly incorporating the expected two-dimensional maps structure into the flow as an inductive bias. We find that HIFLOW is able to reproduce the CAMELS average and standard deviation HI power spectrum within a factor of ≲2, scoring a very high R 2 > 90%. By inverting the flow, HIFLOW provides a tractable high-dimensional likelihood for efficient parameter inference. We show that the conditional HIFLOW on cosmology is successfully able to marginalize over astrophysics at the field level, regardless of the stellar and AGN feedback strengths. This new tool represents a first step toward a more powerful parameter inference, maximizing the scientific return of future HI surveys, and opening a new avenue to minimize the loss of complex information due to data compression down to summary statistics.
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insu-03839638 , version 1 (04-11-2022)

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Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar, et al.. HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows. The Astrophysical Journal, 2022, 937, ⟨10.3847/1538-4357/ac8b09⟩. ⟨insu-03839638⟩
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