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 {sc 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). {sc HIFlow} is trained on the state-of-the-art simulations from
the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS)
project. {sc 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 {sc 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, {sc
HIFlow} provides a tractable high-dimensional likelihood for efficient
parameter inference. We show that the conditional {sc 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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