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NUCLEAR TECHNIQUES, Volume. 48, Issue 5, 050011(2025)

The development and application of deep learning in high-energy nuclear physics

Jingzong ZHANG1,2, Shuang GUO2, Lilin ZHU1, Lingxiao WANG、*, and Guoliang MA2、**
Author Affiliations
  • 1Department of Physics, Sichuan University, Chengdu 610064, China
  • 2(Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China) 3(Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China)
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    Schematic illustration of the fundamental principles of support vector machines

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    A schematic representation of AMPT output results

    Fig. 2. A schematic representation of AMPT output results

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    The fundamental architecture of PointNet[44]

    Fig. 3. The fundamental architecture of PointNet[44]

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    The fundamental architecture of an image-based autoencoder network[60]

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    Jingzong ZHANG, Shuang GUO, Lilin ZHU, Lingxiao WANG, Guoliang MA. The development and application of deep learning in high-energy nuclear physics[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050011

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    Paper Information

    Category: Special Topics on Applications of Machine Learning in Nuclear Physics and Nuclear Data

    Received: Mar. 24, 2025

    Accepted: --

    Published Online: Jun. 26, 2025

    The Author Email: Lingxiao WANG (王凌霄), Guoliang MA (马国亮)

    DOI:10.11889/j.0253-3219.2025.hjs.48.250130

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology