Deep learning approach for identification of HII regions during
  reionization in 21-cm observations
  The upcoming Square Kilometre Array (SKA-Low) will map the distribution of
neutral hydrogen during reionization, and produce a tremendous amount of 3D
tomographic data. These images cubes will be subject to instrumental
limitations, such as noise and limited resolution. Here we present SegU-Net, a
stable and reliable method for identification of neutral and ionized regions in
these images. SegU-Net is a U-Net architecture based convolutional neural
network (CNN) for image segmentation. It is capable of segmenting our image
data into meaningful features (ionized and neutral regions) with greater
accuracy compared to previous methods. We can estimate the true ionization
history from our mock observation of SKA with an observation time of 1000 h
with more than 87 per cent accuracy. We also show that SegU-Net can be used to
recover various topological summary statistics, such as size distributions and
Betti numbers, with a relative difference of only a few per cent. These summary
statistics characterise the non-Gaussian nature of the reionization process.
