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Muon reconstruction with a convolutional neural network in the JUNO detector

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Abstract

Purpose

The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine the neutrino mass ordering and measure neutrino oscillation parameters. A precise muon reconstruction is crucial to reduce one of the major backgrounds induced by cosmic muons.

Methods

This article proposes a novel muon reconstruction method based on convolutional neural network (CNN) models. In this method, the track information reconstructed by the top tracker is used for network training. The training dataset is augmented by applying a rotation to muon tracks to compensate for the limited angular coverage of the top tracker.

Result

The muon reconstruction with the CNN model can produce unbiased tracks with performance that spatial resolution is better than 10 cm and angular resolution is better than 0.6 \({^{\circ }}\). By using a GPU-accelerated implementation, a speedup factor of 100 compared to existing CPU techniques has been demonstrated.

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yan Liu & Wei-Dong Li

  2. Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Yan Liu, Wei-Dong Li, Tao Lin, Wen-Xing Fang, Simon C. Blyth, Ji-Lei Xu, Miao He & Kun Zhang

Authors
  1. Yan Liu
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  2. Wei-Dong Li
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  3. Tao Lin
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  4. Wen-Xing Fang
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  5. Simon C. Blyth
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  6. Ji-Lei Xu
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  7. Miao He
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  8. Kun Zhang
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Corresponding author

Correspondence to Tao Lin.

Additional information

Supported by Strategic Priority Research Program of Chinese Academy of Sciences (XDA10010900) and NSFC (11805223)

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Cite this article

Liu, Y., Li, WD., Lin, T. et al. Muon reconstruction with a convolutional neural network in the JUNO detector. Radiat Detect Technol Methods 5, 364–372 (2021). https://doi.org/10.1007/s41605-021-00259-4

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  • DOI: https://doi.org/10.1007/s41605-021-00259-4

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