Deep learning-based gamma spectroscopic analysis considering multiple variables for in situ applications

Abstract

Gamma spectroscopy in environments with fluctuating temperatures, magnetic fields, and radiation doses can lead to inaccurate material analysis and increased radiation exposure for workers. In this manuscript, we propose a deep learning model that reduces human intervention and remains robust against variables in uncontrolled environments, thereby enhancing the applicability of gamma spectroscopy in the field. The process of establishing a dataset to train, validate, and test the deep learning model involved simultaneous consideration of multiple variables, including gain shifts, detector energy resolutions, the number of mixed radioisotopes (RIs), mixed RI ratios, and statistical fluctuations of the spectrum. The model was trained using multi-label learning to perform RI identification and quantification simultaneously. Specifically, the RI quantification output of our model structure was designed to predict full-energy peak areas and mixed ratios for each RI. Highlighting the importance of a high-performance training dataset, the training and validation results of the gamma spectroscopy model were analyzed in depth. In the test results, organized by RI type and number of mixed RIs, the model demonstrated that following: even under complex conditions with multiple variables, all outcomes for RI identification and quantification were predicted with high accuracy and precision, exceeding 98%.