Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively distinguish between 21cm maps generated by AGN or star-forming galaxies during reionization, aiding in identifying the universe's reionization sources.

Contribution

The study introduces a CNN-based method to classify 21cm maps by source type with high accuracy, improving source identification during reionization.

Findings

CNN achieves 92-100% accuracy in source classification.

Foreground removal significantly impacts classification accuracy.

Future SKA surveys can effectively differentiate reionization sources.

Abstract

Active Galactic Nuclei (AGN) and star-forming galaxies are leading candidates for being the luminous sources that reionized our Universe. Next-generation 21cm surveys are promising to break degeneracies between a broad range of reionization models, hence revealing the nature of the source population. While many current efforts are focused on a measurement of the 21cm power spectrum, some surveys will also image the 21cm field during reionization. This provides further information with which to determine the nature of reionizing sources. We create a Convolutional Neural Network (CNN) that is efficiently able to distinguish between 21cm maps that are produced by AGN versus galaxies scenarios with an accuracy of 92-100%, depending on redshift and neutral fraction range. An exception to this is when our Universe is highly ionized, since the source models give near-identical 21cm maps in that case. When adding thermal noise from typical 21cm experiments, the classification accuracy depends strongly on the effectiveness of foreground removal. Our results show that if foregrounds can be removed reasonably well, SKA, HERA and LOFAR should be able to discriminate between source models with greater accuracy at a fixed redshift. Only future SKA 21cm surveys are promising to break the degeneracies in the power spectral analysis.