English(EN) Peak-Based Nuclide Identification in HPGe $\gamma$-Spectrometry with Machine Learning and SHAP

机器学习模型自动识别伽马谱学中的核素

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种机器学习模型，用于自动识别高纯锗伽马谱中的核素，这一过程通常需要大量专家时间。该模型在65种同位素上进行了训练，F1分数达到了0.97，优于传统软件的0.84。Shapley Additive Explanations被用来证明该模型依赖于物理相关的光峰进行预测，从而验证了其方法。 AI

排序理由该集群包含一篇学术论文，详细介绍了使用机器学习进行核素识别的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Samuel Emmons, Kelly Truax, Maurice Lonsway, Bruce Pierson, Brian Archambault · 2026-06-16 04:00

Peak-Based Nuclide Identification in HPGe $\gamma$-Spectrometry with Machine Learning and SHAP

arXiv:2606.14874v1 Announce Type: cross Abstract: 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 (…

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Peak-Based Nuclide Identification in HPGe $\gamma$-Spectrometry with Machine Learning and SHAP

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