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Updated: May 16, 2025

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中性网:深度神经网络的应用,用于脉冲形状歧视,用于基于加速器的中子源

Richard L Garnett¹, Ariel Amsellem², Arun Persaud²

¹Department of Physics and Astronomy, McMaster University, Hamilton, ON, L8S 4K1, Canada.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

|May 14, 2025

概括

机器学习通过改善中子-马区分来增强中子检测. 转移学习显著提高了不同中子源的分类准确性,即使在低能量的情况下也实现了高性能.

科学领域:

核物理核物理是核物理的.
应用机器学习应用机器学习
辐射检测仪器仪器仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表

背景情况:

机器学习 (ML) 越来越多地应用于复杂的分类任务,包括辐射检测中的脉冲形状歧视 (PSD).
中子子检测和表征在各种科学和安全应用中至关重要.

研究的目的:

通过改进脉冲形状歧视来推进中子检测的ML应用.
调查基于ML的不同中子能光谱的中子-玛区分技术.

主要方法:

利用各种中子源 (DD,DT, (α,n),自发裂变) 来产生不同的中子能量分布.
研究了光倍子管 (PMT) 偏差电压对歧视性能的影响.
应用转移学习技术,以解决不同类型中子源的分类挑战.
分析了中子分类性能的能量依赖性.

主要成果:

中子分类性能在1500 V PMT偏差处达到顶峰,在1E-6虚假阳性率 (FPR) 上识别了81%的验证中子.
转移学习在训练和测试不同中子源时显著提高了分类准确性.
在1E-6 FPR的速度下,实现了将中子和光子群体的分离到30 keVee.
对于在1E-6 FPR.的252Cf源,证明了12.5%的整体内在中子检测效率.

结论:

优化PMT偏差电压对于基于ML的中子-马区分至关重要.
转移学习有效地将各种来源中中子检测的ML模型通用化.
开发的技术使敏感的中子检测具有高特异性,即使在低能量的.

关键词:

机器学习机器学习中子的玛分离.脉冲形状的歧视脉冲形状的歧视

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