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Neutron-gamma pulse shape discrimination for EJ301 liquid scintillator based on machine learning

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Abstract

In fast neutron multiplicity counting measurement, misclassification of γ signals and loss of neutrons introduce significant measurement errors. To address these problems, machine learning (ML) algorithms were employed to improve the n/γ discrimination of liquid scintillators. A dual-scintillator time-of-flight device combined with charge comparison (CC) method was used to select reliable datasets from the D-T neutron generator. Decision Tree, Random Forest, and Back-Propagation Neural Network (BPNN) were developed and compared with the CC method. The CC method and ML algorithms were validated using 137Cs sources. The results showed that the ML algorithms had effective n/γ discrimination capabilities. The BPNN exhibited the highest DERγ (1.26%) and DERn (1.64%) discrimination performance, which reduced neutron loss and γ misclassification. In addition, the trained BPNN was used in practical measurement.

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Acknowledgements

This work was a project supported by the National Natural Science Foundation of China (11975121) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX22_0354).

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Authors and Affiliations

  1. College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jiahao Xu, Wei Cheng, Wenbao Jia, Qingbo Chen, Ronghua Zhang, Yan Zhang & Can Cheng

  2. Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, 215000, China

    Wenbao Jia

  3. Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jiahao Xu, Wei Cheng, Wenbao Jia, Qingbo Chen, Ronghua Zhang, Yan Zhang & Can Cheng

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  1. Jiahao Xu
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  2. Wei Cheng
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  3. Wenbao Jia
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  4. Qingbo Chen
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Correspondence to Wenbao Jia.

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Xu, J., Cheng, W., Jia, W. et al. Neutron-gamma pulse shape discrimination for EJ301 liquid scintillator based on machine learning. J Radioanal Nucl Chem 333, 905–916 (2024). https://doi.org/10.1007/s10967-023-09327-z

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