Kavli Affiliate: David N. Spergel
| First 5 Authors: Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt, David N. Spergel, Daniel Anglés-Alcázar
| Summary:
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 ($Omega_{m}$ and $sigma_{8}$) and designed
using a class of normalizing flow models, the Masked Autoregressive Flow (MAF).
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 2D 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 (Pk) within a factor of $lesssim$ 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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