A Neural Network Approach to Multi-radionuclide TDCR Beta Spectroscopy

• URL: http://arxiv.org/abs/2509.03137v1
• Date: Wed, 03 Sep 2025 08:40:02 GMT
• Title: A Neural Network Approach to Multi-radionuclide TDCR Beta Spectroscopy
• Authors: Li Yi, Qian Yang,
• Abstract summary: Liquid scintillation triple-to-doubly coincident ratio (TDCR) spectroscopy is widely adopted as a standard method for radionuclide quantification.<n>Here, we present an Artificial Intelligence framework that combines numerical spectral simulation and deep learning for standard-free automated analysis.
• Score: 12.470638217209851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Liquid scintillation triple-to-doubly coincident ratio (TDCR) spectroscopy is widely adopted as a standard method for radionuclide quantification because of its inherent advantages such as high precision, self-calibrating capability, and independence from radioactive reference sources. However, multiradionuclide analysis via TDCR faces the challenges of limited automation and reliance on mixture-specific standards, which may not be easily available. Here, we present an Artificial Intelligence (AI) framework that combines numerical spectral simulation and deep learning for standard-free automated analysis. $\beta$ spectra for model training were generated using Geant4 simulations coupled with statistically modeled detector response sampling. A tailored neural network architecture, trained on this dataset covering various nuclei mix ratio and quenching scenarios, enables autonomous resolution of individual radionuclide activities and detecting efficiency through end-to-end learning paradigms. The model delivers consistent high accuracy across tasks: activity proportions (mean absolute error = 0.009), detection efficiencies (mean absolute error = 0.002), and spectral reconstruction (Structural Similarity Index = 0.9998), validating its physical plausibility for quenched $\beta$ spectroscopy. This AI-driven methodology exhibits significant potential for automated safety-compliant multiradionuclide analysis with robust generalization, real-time processing capabilities, and engineering feasibility, particularly in scenarios where reference materials are unavailable or rapid field analysis is required.

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