Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID.

中文翻译：

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用于自动核素识别挑战的卷积神经网络

随着机器学习模型的可访问性的增加，放射性同位素识别 (RIID) 算法的改进引起了人们的兴趣。已经开发出基于卷积神经网络 (CNN) 的模型来识别伽马光谱中不稳定核素的任意混合物。为此，还开发了训练数据的模拟和预处理方法。一维多类、多标签 CNN 的实现证明了对真实光谱的良好泛化，但统计数据较差，增益变化显着。还表明，即使是基本的 CNN 架构也证明在重屏蔽和近源几何的挑战性条件下对 RIID 是可靠的，并且可以扩展到实用 RIID 的通用解决方案。