Identifying reionization sources from 21 cm maps using Convolutional Neural Networks. Issue 2 (3rd December 2018) - Hassan, Sultan; Liu, Adrian; Kohn, Saul; La Plante, Paul
ABSTRACT: Active galactic nuclei (AGNs) and star-forming galaxies are leading candidates for being the luminous sources that reionized our Universe. Next-generation 21 cm 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 21 cm power spectrum, some surveys will also image the 21 cm field during reionization. This provides further information with which to determine the nature of reionizing sources. We create a Convolutional Neural Network that is efficiently able to distinguish between 21 cm maps that are produced by AGN versus star-forming galaxies scenarios with an accuracy of 92–100 per cent, 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 21 cm maps in that case. When adding thermal noise from typical 21 cm experiments, the classification accuracy depends strongly on the effectiveness of foreground removal. Our results show that if foregrounds can be removed reasonably well, Square Kilometer Array (SKA), Hydrogen Epoch of Reionization Array, and Low Frequency Array should be able to discriminate between source models with greater accuracy at a fixed redshift. Only future SKA 21 cm surveys are promising to break the degeneracies in the power spectral analysis.