用机器学习自动识别高纯锗伽马谱中的放射性核素，提升准确率与效率。
Peak-Based Nuclide Identification in HPGe $γ$-Spectrometry with Machine Learning and SHAP
- 基于人工拟合的峰特征训练机器学习模型进行核素识别
- 模型F1得分达0.97，显著优于传统软件的0.84
- 通过SHAP分析揭示模型依赖物理上合理的峰信息，可解释性强
高纯锗伽马谱通常需领域专家耗时分析。谱图中的光峰需精细拟合并借助数值方法完成核素识别（NID）与定量。当分析大量样本时，及时准确决策面临挑战。本研究采用监督学习方法构建自动化核素识别工具，以改进分析软件初始建议的核素列表，从而更高效推动后续定量。我们利用65种同位素组合的实验谱图数据，训练模型将专家拟合的光峰映射至核素识别结果。最佳模型在相同65种同位素库下的F1得分为0.97，远超传统软件的0.84。最后，通过Shapley Additive Explanations（SHAP）揭示关键输入特征，发现模型在预测中依赖于物理上有意义的光峰，具备良好的可解释性。
原文摘要 · Abstract (English)
High-purity germanium gamma spectra often require time-consuming analyses from subject matter experts. Photopeaks within these spectra are carefully fitted and numerical methods are employed to assist with nuclide identification (NID) and quantification. Amending the list of nuclides identified by analysis software can be nontrivial. When many samples need to be analyzed, it is therefore challenging to make timely and correct decisions. Supervised machine-learning-based NID can serve as an expert-informed, automated tool to improve the initial set of radionuclides suggested to an analyst and more effectively drive subsequent quantification. To that end, we implemented machine learning models that map photopeaks carefully fitted by analysts to NID results for experimental spectra containing various isotopic combinations drawn from a set of 65 isotopes. The best model achieved an F1 score of 0.97, markedly surpassing the F1 score of 0.84 achieved by traditional software when compared using a nuclide library comprising the same 65 isotopes assessed by the models. Finally, we illustrated the most important input features for model predictions using Shapley Additive Explanations. These explanations revealed that the models use physically relevant photopeaks when making predictions for the isotopes in our nuclide library.
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