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DeepNSI:用于非法物质检测的实验光中子光谱中的元素识别

C Besnard-Vauterin¹, V Blideanu¹, B Rapp²

¹Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), F-91120, Palaiseau, France.

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

|June 28, 2025

概括

深度学习框架DeepNSI使用光子诱导的中子光谱准确识别元素组成,用于检测非法材料. 它提供可靠的元素分析与不确定性估计,以提高决策信心.

科学领域:

核物理与工程核物理与工程
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

光子探测系统对于安全应用至关重要,但准确的元素分析仍然具有挑战性.
检测和氧等轻元素对于识别爆炸物和化学威胁至关重要.
现有的方法往往缺乏复杂的检查场景所需的精度和解释性.

研究的目的:

开发一个强大的深度学习框架,DeepNSI,用于从光子诱导的中子光谱中识别元素组成.
增强对光元素的检测能力,对于安全应用至关重要.
为可靠的决策提供可解释的结果与不确定性量化.

主要方法:

开发了DeepNSI,这是一组特定元素的卷积神经网络.
在模拟和实验光中子光谱的混合数据集上训练模型.
纳入预测不确定性的蒙特卡罗脱落和用于光谱重建的非负最小平方 (NNLS).

主要成果:

在现实数据上展示了强大的元素识别,包括有机化合物和屏蔽材料.
实现了轻元素的可靠检测,爆炸物和化学威胁的关键签名.
NNLS趋势反映了有意义的组成差异,支持可解释的分析.

结论:

DeepNSI提供了一种可靠的,基于机器学习的方法,用于光子查询系统中的元素分析.
该框架提供可解释的结果与不确定性估计,增强对安全应用程序的信心.
DeepNSI在光子中子谱学领域取得了进展,用于材料检查和威胁检测.

关键词:

深度学习是一种深度学习.美国国土安全部中子子光谱分析.光子中子光谱学光子中子谱学

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