Low-resolution gamma-ray spectrum analysis using comprehensive training set and deep ResNet architecture
Low-resolution gamma-ray spectrum analysis using comprehensive training set and deep ResNet architecture
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DOI：
10.1016/j.nima.2023.168135
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
R. Zhao;Na Liu
中科院分区：
文献类型：
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作者：
R. Zhao;Na Liu
针对现有基于人工神经网络的伽玛能谱分析方法在训练集和结构上的局限性，提出了一种构造综合训练集的方法和一种改进的ResNet结构来进行低分辨率伽玛能谱分析。构造的训练集再现的形状特征多样性的光谱采集从真实的检测方案，通过使用各种参数设置在蒙特卡洛模拟。提出的ResNet构成了有史以来应用于伽马射线谱分析的最大和最深的架构，包含51层和超过107个参数。对模拟和实测光谱的测试结果表明，该网络的平均精度和F1得分的关键性能值明显优于卷积神经网络和全连接网络。此外，我们的方法提供了弱射线识别，假峰歧视，重叠峰分辨率。该研究证明了将深度学习应用于伽马射线谱分析的可行性，并介绍了一种实现通用、准确、灵敏和可靠的伽马射线谱分析的方法。
Considering limitations of training sets and architectures in existing methods for gamma-ray spectrum analysis based on artificial neural networks, we propose a method for constructing a comprehensive training set and a modified ResNet architecture to perform low-resolution gamma-ray spectrum analysis. The constructed training set reproduces the shape feature diversity of spectra acquired from real detection scenarios by using various parameter settings in Monte Carlo simulations. The proposed ResNet constitutes the largest and deepest architecture ever applied to gamma-ray spectrum analysis, containing 51 layers and more than 107parameters. Results from tests on simulated and measured spectra show that the key performance values of average precision and F1 score of the proposed network are substantially better than those of a convolutional neural network and a fully connected network. Moreover, our approach provides weak ray identification, fake peak discrimination, and overlapping peak resolution. This study demonstrates the feasibility of applying deep learning to gamma-ray spectrum analysis and introduces an approach to achieve general, accurate, sensitive, and reliable gamma-ray spectrum analysis.