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Machine-learning based gamma-ray spectrum analysis for initial response on nuclear security event; Development of training data set by detector simulation and usability evaluation for radioisotope identification algorism

Kimura, Yoshiki  ; Tsuchiya, Kenichi*

A nuclear security event involving nuclear and other radioactive materials out of regulatory control (MORC) has potential severe consequence on public health, environments, economics and society. When a nuclear security event caused by MORC would be occurred, it is essential to identify the hazardous substances such as nuclear materials and radioisotopes as the initial response activity at the event scene. In this study, automated radioisotope identification algorism by Machine-Learning (ML) based gamma-ray spectrum analysis using handheld type detectors have been developed. The training data set for ML-based algorism has been developed based on detector simulation and the usability of the simulation-based data set for ML model training to perform radioisotope identification has been discussed.

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