Automatic and fast identification of gamma-ray-emitting radionuclides is a challenge in the field of nuclear safety, especially in case of emergency, since it requires complex calculations and often the knowledge of experts to interpret the data. We present a development of an automatic identification method based on convolutional neural networks (CNNs) as a new tool to analyze gamma-ray spectra in real time, which uses not only photoelectric peaks but also extracts all discriminant features in the spectrum, such as Compton structures, for instance. The original approach relies on the training of the CNN with a fully synthetic database, built by means of a Monte Carlo simulation with Geant4 combined with a detailed analytical detector response model. The algorithm and training method are evaluated to identify radionuclides in measurements of the mixtures of sources acquired with Caliste, a fine-pitch CdTe imaging spectrometer. The neural network is able to discriminate each element in an arbitrary mixture very quickly with high accuracy.

中文翻译：

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伽马射线谱中放射性核素的自动实时识别：一种基于合成数据集训练的卷积神经网络的新方法

自动和快速识别发射伽马射线的放射性核素是核安全领域的一个挑战，尤其是在紧急情况下，因为它需要复杂的计算和专家的知识来解释数据。我们提出了一种基于卷积神经网络 (CNN) 的自动识别方法的开发，作为实时分析伽马射线光谱的新工具，该方法不仅使用光电峰，还提取光谱中的所有判别特征，例如康普顿结构，例如。原始方法依赖于使用完全合成数据库对 CNN 进行训练，该数据库是通过使用 Geant4 的蒙特卡罗模拟结合详细的分析探测器响应模型构建的。对算法和训练方法进行评估，以识别使用 Caliste（一种细间距 CdTe 成像光谱仪）获取的混合源的测量中的放射性核素。神经网络能够以高精度快速区分任意混合物中的每个元素。