Hiflow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing
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 (Ωm and σ8) 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 R2 > 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.
Description
Keywords
Astrophysics, Astrophysics, Cosmology, Reionization, Early universe
Citation
Hassan, S. et al. (2022). Hiflow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows. Astrophysical Journal, 937(2), 83. https://doi.org/10.3847/1538-4357/ac8b09