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

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Abstract

We propose a deep learning analysis technique with a 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 = 6–13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. 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 great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

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Acknowledgments

The authors thank Kyungjin Ahn, Hyunbae Park, Dongsu Bak, Sangnam Park, David Parkinson, Jacobo Asorey, and an anonymous reviewer for helpful discussion and comments. The authors were supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2018R1A6A1A06024977). Computational data were transferred through a high-speed network provided by the Korea Research Environment Open NET-work (KREONET).

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Authors and Affiliations

  1. Department of Physics, University of Seoul, Seoul, 02504, Korea

    Yungi Kwon & Inkyu Park

  2. Natural Science Research Institute, University of Seoul, Seoul, 02504, Korea

    Sungwook E. Hong

  3. Natural Science Research Institute, University of Seoul, Seoul, 02504, Korea

    Inkyu Park

Authors
  1. Yungi Kwon
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  2. Sungwook E. Hong
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  3. Inkyu Park
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Correspondence to Sungwook E. Hong.

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Kwon, Y., Hong, S.E. & Park, I. Deep-Learning Study of the 21-cm Differential Brightness Temperature During the Epoch of Reionization. J. Korean Phys. Soc. 77, 49–59 (2020). https://doi.org/10.3938/jkps.77.49

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  • DOI: https://doi.org/10.3938/jkps.77.49

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