Constraining the Evolution of Epoch of Reionization by Deep-Learning the 21-cm Differential Brightness Temperature

  • Kwon, Yungi (Department of Physics, University of Seoul) ;
  • Hong, Sungwook E. (Natural Science Research Institute, University of Seoul)
  • 발행 : 2019.10.14

초록

We develop a novel technique that can constrain the evolutionary track of the epoch of reionization (EoR) by applying the convolutional neural network (CNN) to the 21-cm differential brightness temperature. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm map between z=6-13. We design a CNN architecture that predicts the volume-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction has a good agreement with its truth value even after smoothing the 21-cm map with somewhat realistic choices of beam size and the frequency bandwidth of the Square Kilometre Array (SKA). Our technique could be further utilized to denoise the 21-cm map or constrain the properties of the radiation sources.

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