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基于机器学习的多中子探测技术

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中子滴线区的丰中子原子核结构是当前放射性核束物理研究的前沿热点之一.通过直接探测这些不稳定原子核衰变中发射的中子,不仅能提取核内部的多中子关联,也为丰中子核物质的性质研究提供重要的线索.为满足开展多中子探测实验的需求,本工作发展了基于机器学习的多中子识别算法,以大量的模拟数据作为训练样本,构建深度神经网络来逐事件判定反应的中子数,并进一步挑选出真实中子.本工作的结果表明,机器学习算法的四中子探测效率为～15％,传统算法为～1％,机器学习算法能将四中子探测效率显著提升10倍以上,有望应用到多中子探测实验中.
Multi-neutron Detection Based on Machine Learning
The structure of neutron-rich nuclei in the neutron drip line region is one of the frontiers of the Radioactive Ion Beam physics.By directly detecting the neutrons emitted during their decay,the multi-neutron correlations of the nucleus can be extracted,which also provides critical information for the study of the properties of neutron-rich nuclear matter.In order to meet the requirements of conducting multi-neutron detection experiments,we developed a machine-learning-based multi-neut-ron recognition algorithm.We constructed a deep neural network to determine the number of incident neutrons event by event,and to further select the real neutron signals.The results of this work indicate that the detection efficiency of the machine learning algorithm is～15％,whereas that of the traditional algorithm is～1％.The machine learning algorithm can signific-antly improve four-neutron detection efficiency by more than 10 times,and is expected to be applied to multi-neutron detec-tion experiments.

neutron-rich nucleimulti-neutron detectionmachine learningdeep neural network

杜泽宇、黄思维、杨再宏、李奇特、边佳伟

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北京大学物理学院,北京 100871

北京大学核物理与核技术国家重点实验室,北京 100871

丰中子核 多中子探测 机器学习 深度神经网络

国家重点研发计划项目国家重点研发计划项目国家自然科学基金资助项目北京大学核物理与核技术国家重点实验室自主科研课题资助项目北京大学核物理与核技术国家重点实验室自主科研开放课题State Key Laboratory of Nuclear Physics and Technology,Peking UniversityState Key Laboratory of Nuclear Physics and Technology,Peking University

2022YFA16051002023YFE010150012275006NPT2022ZZ02NPT2020KFY06NPT2022ZZ02NPT2020KFY06

2024

10.11804/NuclPhysRev.41.2023CNPC64

原子核物理评论
中国科学院近代物理研究所，中国核物理学会

原子核物理评论

CSTPCD北大核心
影响因子：0.181
ISSN：1007-4627
年,卷(期)：2024.41(1)