Deep-Learning Study of the 21cm Differential Brightness Temperature
  During the Epoch of Reionization
  We propose a deep learning analyzing technique with convolutional neural
network (CNN) to predict the evolutionary track of the Epoch of Reionization
(EoR) from the 21-cm differential brightness temperature tomography images. We
use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to
produce mock 21-cm maps between $z=6 \sim 13$. We then apply two observational
effects into those 21-cm maps, such as instrumental noise and limit of (spatial
and depth) resolution somewhat suitable for realistic choices of the Square
Kilometre Array (SKA). We design our deep learning model with CNN to predict
the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The
estimated neutral fraction from our CNN model has a great agreement with its
true value even after coarsely smoothing with broad beamsize and frequency
bandwidth, and also heavily covered by noise with narrow. Our results have
shown that deep learning analyzing method has a large potential to efficiently
reconstruct the EoR history from the 21-cm tomography surveys in future.
