HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro,, Seda Bilaloglu, Asena Derin Cengiz, Siyu He, Laurence Perreault Levasseur,, Shirley Ho

TL;DR
This paper introduces a Wasserstein GAN model that generates high-resolution 3D cosmic neutral hydrogen distributions, matching detailed statistical properties of simulations and enabling faster predictions for astrophysical surveys.
Contribution
The paper presents a novel WGAN approach to efficiently generate realistic cosmic HI distributions, outperforming traditional models and reducing computational costs significantly.
Findings
WGAN samples match the statistical properties of IllustrisTNG simulations
Generated HI abundance spans nine orders of magnitude
WGAN produces samples orders of magnitude faster than hydrodynamic simulations
Abstract
One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those observations can shed light on fundamental astrophysical questions only if accurate theoretical predictions are available. In order to maximize the scientific return of these surveys, those predictions need to include different observables and be precise on non-linear scales. Currently, one of the best ways to achieve this is via cosmological hydrodynamic simulations; however, the computational cost of these simulations is high -- tens of millions of CPU hours. In this work, we use Wasserstein Generative Adversarial Networks (WGANs) to generate new high-resolution () 3D realizations of cosmic HI at . We do so by sampling from a 100-dimension manifold, learned by the generator, that characterizes the fully…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Gamma-ray bursts and supernovae · Astrophysics and Cosmic Phenomena
MethodsConvolution · Wasserstein GAN
