Removing Astrophysics in 21 cm Maps with Neural Networks
Abstract
Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 ≤ z ≤ 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales ≃2 Mpc^{1}. We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters.
 Publication:

The Astrophysical Journal
 Pub Date:
 January 2021
 DOI:
 10.3847/15384357/abd245
 arXiv:
 arXiv:2006.14305
 Bibcode:
 2021ApJ...907...44V
 Keywords:

 Cosmology;
 Cold dark matter;
 Dark matter;
 Dark matter distribution;
 H I line emission;
 Intergalactic medium;
 Cosmological evolution;
 Convolutional neural networks;
 Largescale structure of the universe;
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 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 17 pages, 10 figures