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 (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
The increasing maturity of machine learning techniques and the general drive towards automation across scientific domains are enabling the application of AI to complex analytical tasks like gamma spectrometry.
Automating nuclide identification improves the speed and accuracy of critical analyses in fields like nuclear safety, environmental monitoring, and scientific research, reducing reliance on manual expert labor.
Nuclide identification processes move from labor-intensive, expert-dependent methods towards more automated, AI-assisted workflows, potentially accelerating data analysis and decision-making.
- · Machine Learning Engineers
- · Nuclear Science Laboratories
- · Environmental Monitoring Agencies
- · Traditional Manual Spectrometry Analysis Services
Faster and more consistent nuclide identification in HPGe gamma spectra.
Reduced operational costs and improved throughput for laboratories conducting large-scale radiological analyses.
Enhanced overall safety and regulatory compliance due to more timely and accurate detection of specific radionuclides.
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