基于人工神经网络的中子光谱测定,使用液晶闪光探测器EJ-301进行测定
1Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, China.
Radiation protection dosimetry
|September 16, 2024
概括
这项研究展示了一种使用液体闪探测器和线性人工神经网络 (ANN) 来测量中子光谱的新方法. 该ANN准确地从探测器输出的中子光谱展开,通过加利福尼亚-252测量进行验证.
科学领域:
- 核物理 核物理 核物理
- 探测器技术 探测器技术
- 计算物理 计算物理
背景情况:
- 准确的中子光谱测量对于核应用至关重要.
- 液体闪探测器为中子检测提供了一个可行的解决方案.
- 传统的中子光谱展开方法可能是复杂和耗时的.
研究的目的:
- 开发和验证一种使用线性人工神经网络 (ANN) 进行中子光谱展开的新方法.
- 准确识别和展开来自EJ-301液体闪光探测器光输出的中子光谱.
- 评估使用ANN用于实时中子光谱测定的可行性.
主要方法:
- 蒙特卡洛模拟 (FLUKA) 用于为EJ-301探测器生成响应函数.
- 一个线性人工神经网络 (ANN) 使用模拟探测器响应函数进行训练.
- 经过训练的ANN模型被应用于从模拟和实验数据中展开中子光谱.
- 采用电荷比较方法,从玛射线中分离中子诱导的信号.
主要成果:
- 该ANN模型准确地从模拟光输出分布中展开了中子光谱.
- 使用252Cf源进行的实验测量显示,展开的中子光谱与参考数据之间有很好的一致性.
- 该方法在从液体闪光探测器输出中识别中子频谱信息方面表现出高精度.
结论:
- 训练有模拟响应函数的线性ANN为中子光谱展开提供了可行和准确的方法.
- 这种方法为确定未知的中子光谱提供了一个有希望的替代方案.
- 开发的模型是先进中子光谱确定应用的潜在候选者.
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