通过使用BP神经网络扩大能量值的下限,进行中子-马区分
概括
本研究介绍了一种使用脉冲形状歧视 (PSD) 和人工神经网络的先进中子-马歧视方法. 这项新技术显著改善了低能中子探测和实验测量的数据利用.
科学领域:
- 核物理 核物理 核物理
- 实验中子检测的实验中子检测.
- 科学测量中的人工智能
背景情况:
- 中子-马区分对于准确的中子测量至关重要,特别是在低能量的情况下.
- 现有的方法在降低能源值和最大限度地利用数据方面存在局限性.
研究的目的:
- 开发一种改进的中子-马分辨方法.
- 为了提高低能中子的检测能力.
- 提高实验中子测量的效率和适用性.
主要方法:
- 使用脉冲形状歧视 (PSD) 结合反向传播 (BP) 人工神经网络.
- 实现一个神经网络与20个神经元的隐藏层.
- 培训并将开发的方法应用于实验中子数据.
主要成果:
- 对于中子和玛射线信号,达到99.93%的高分辨率准确度.
- 将能量值从350 keV降低到70 keV.
- 获取数据的利用率从60%提高到99.9%以上.
结论:
- 开发的PSD和BP-ANN方法有效地克服了中子-马歧视中的硬件限制.
- 这种方法显著扩大了中子检测的较低能量值.
- 该方法和训练有素的神经网络可适应各种实验中子测量.
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