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We propose a deep learning analyzing technique with convolutional neuralnetwork (CNN) to predict the evolutionary track of the Epoch of Reionization(EoR) from the 21-cm differential brightness temperature tomography images. Weuse 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, toproduce mock 21-cm maps between $z=6 \sim 13$. We then apply two observationaleffects into those 21-cm maps, such as instrumental noise and limit of (spatialand depth) resolution somewhat suitable for realistic choices of the SquareKilometre Array (SKA). We design our deep learning model with CNN to predictthe sliced-averaged neutral hydrogen fraction from the given 21-cm map. Theestimated neutral fraction from our CNN model has a great agreement with itstrue value even after coarsely smoothing with broad beamsize and frequencybandwidth, and also heavily covered by noise with narrow. Our results haveshown that deep learning analyzing method has a large potential to efficientlyreconstruct the EoR history from the 21-cm tomography surveys in future.

Deep-Learning Study of the 21cm Differential Brightness Temperature During the Epoch of Reionization