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Machine learning in nuclear physics at low and intermediate energies

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Abstract

Machine learning (ML) is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing tasks. In this review, we first briefly introduce the different methodologies used in ML algorithms and techniques. As a snapshot of many applications by ML, some selected applications are presented, especially for low- and intermediate-energy nuclear physics, which include topics on theoretical applications in nuclear structure, nuclear reactions, properties of nuclear matter, and experimental applications in event identification/reconstruction, complex system control, and firmware performance. Finally, we present a summary and outlook on the possible directions of ML use in low-intermediate energy nuclear physics and possible improvements in ML algorithms.

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Authors and Affiliations

  1. Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai, 200433, China

    Wanbing He & Yugang Ma

  2. Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai, 200438, China

    Wanbing He & Yugang Ma

  3. School of Science, Huzhou University, Huzhou, 313000, China

    Qingfeng Li

  4. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China

    Qingfeng Li

  5. School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China

    Zhongming Niu

  6. State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing, 100871, China

    Junchen Pei

  7. Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou, 516000, China

    Junchen Pei

  8. Department of Nuclear Physics, China Institute of Atomic Energy, Beijing, 102413, China

    Yingxun Zhang

  9. Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, Guilin, 541004, China

    Yingxun Zhang

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  1. Wanbing He
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  3. Yugang Ma
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Corresponding authors

Correspondence to Wanbing He, Qingfeng Li, Yugang Ma, Zhongming Niu, Junchen Pei or Yingxun Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11875070, 11875323, 12275359, 11875125, 12147219, U2032145, 11705163, 11790320, 11790323, 11790325, 11975032, 11835001, 11935001, 11890710, 12147101, 11835002, 11705031, and 11961141003), the National Key R&D Program of China (Grant Nos. 2018 YFA0404404, 2018YFA0404403, and 2020YFE0202001), the Continuous Basic Scientific Research Project (Grant No. WDJC-2019-13), the Continuous Basic Scientific Research Project (Grant No. WDJC-2019-13), the funding of China Institute of Atomic Energy (Grant No. YZ222407001301), the Leading Innovation Project of the China National Nuclear Corporation (Grant Nos. LC192209000701, and LC202309000201), and the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2020B0301030008).

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He, W., Li, Q., Ma, Y. et al. Machine learning in nuclear physics at low and intermediate energies. Sci. China Phys. Mech. Astron. 66, 282001 (2023). https://doi.org/10.1007/s11433-023-2116-0

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