Identifying Reionization Sources from 21cm Maps using Convolutional
  Neural Networks
  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.
