HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows Article Swipe

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Sultan Hassan , Francisco Villaescusa-Navarro , B. D. Wandelt , David N. Spergel , Daniel Anglés‐Alcázar , Shy Genel , Miles Cranmer , Greg L. Bryan , Romeel Davé , Rachel S. Somerville , Michael Eickenberg , Desika Narayanan , Shirley Ho , Sambatra Andrianomena ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2110.02983

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 (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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Artificial Intelligence

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Cosmology Physics Inference Astrophysics Flow (mathematics) Field (mathematics) Statistical physics Computer science Artificial intelligence Mathematics Mechanics Pure mathematics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2110.02983
PDF: https://arxiv.org/pdf/2110.02983
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Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2110.02983

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Title: HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows

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Language: en

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Publication year: 2021

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Publication date: 2021-10-06

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Authors: Sultan Hassan, Francisco Villaescusa-Navarro, B. D. Wandelt, David N. Spergel, Daniel Anglés‐Alcázar, Shy Genel, Miles Cranmer, Greg L. Bryan, Romeel Davé, Rachel S. Somerville, Michael Eickenberg, Desika Narayanan, Shirley Ho, Sambatra Andrianomena

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Landing page: https://arxiv.org/abs/2110.02983

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PDF URL: https://arxiv.org/pdf/2110.02983

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Concepts: Cosmology, Physics, Inference, Astrophysics, Flow (mathematics), Field (mathematics), Statistical physics, Computer science, Artificial intelligence, Mathematics, Mechanics, Pure mathematics

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Cited by: 1

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abstract_inverted_index.explicitly	98
abstract_inverted_index.generative	39
abstract_inverted_index.inference,	196
abstract_inverted_index.inference.	156
abstract_inverted_index.likelihood	152
abstract_inverted_index.maximizing	197
abstract_inverted_index.regardless	176
abstract_inverted_index.represents	187
abstract_inverted_index.scientific	199
abstract_inverted_index.strengths.	183
abstract_inverted_index.Simulations	85
abstract_inverted_index.compression	220
abstract_inverted_index.conditional	161
abstract_inverted_index.conditioned	49
abstract_inverted_index.efficiently	30
abstract_inverted_index.information	6, 216
abstract_inverted_index.large-scale	14
abstract_inverted_index.marginalize	169
abstract_inverted_index.normalizing	62
abstract_inverted_index.simulations	76
abstract_inverted_index.statistics.	224
abstract_inverted_index.Astrophysics	81
abstract_inverted_index.astrophysics	171
abstract_inverted_index.cosmological	3
abstract_inverted_index.information.	33
abstract_inverted_index.successfully	166
abstract_inverted_index.astrophysical	5
abstract_inverted_index.incorporating	99
abstract_inverted_index.Autoregressive	67
abstract_inverted_index.high-dimensional	151
abstract_inverted_index.state-of-the-art	75
cited_by_percentile_year.max	94
cited_by_percentile_year.min	89
countries_distinct_count	0
institutions_distinct_count	14
citation_normalized_percentile.value	0.59842835
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False