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Open AccessArticle

Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning

1
Applied Artificial Intelligence Laboratory, Korea Atomic Energy Research Institute, Daejeon 34507, Korea
2
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
3
Department of Environmental Radiation Monitoring and Assessment, Korea Institute of Nuclear Safety, Daejeon 34142, Korea
4
Decommissioning Technology Research Division, Korea Atomic Energy Research Institute, Daejeon 34507, Korea
5
Accelerator Development and Research Division, Korea Atomic Energy Research Institute-Korea Multi-Purpose Accelerator Complex, Gyeongju-si 38180, Korea
*
Authors to whom correspondence should be addressed.
Academic Editors: Andrea Malizia and Tzany Kokalova Wheldon
Sensors 2021, 21(3), 684; https://doi.org/10.3390/s21030684
Received: 30 November 2020 / Revised: 18 January 2021 / Accepted: 19 January 2021 / Published: 20 January 2021
Although plastic scintillation detectors possess poor spectroscopic characteristics, they are extensively used in various fields for radiation measurement. Several methods have been proposed to facilitate their application of plastic scintillation detectors for spectroscopic measurement. However, most of these detectors can only be used for identifying radioisotopes. In this study, we present a multitask model for pseudo-gamma spectroscopy based on a plastic scintillation detector. A deep- learning model is implemented using multitask learning and trained through supervised learning. Eight gamma-ray sources are used for dataset generation. Spectra are simulated using a Monte Carlo N-Particle code (MCNP 6.2) and measured using a polyvinyl toluene detector for dataset generation based on gamma-ray source information. The spectra of single and multiple gamma-ray sources are generated using the random sampling technique and employed as the training dataset for the proposed model. The hyperparameters of the model are tuned using the Bayesian optimization method with the generated dataset. To improve the performance of the deep learning model, a deep learning module with weighted multi-head self-attention is proposed and used in the pseudo-gamma spectroscopy model. The performance of this model is verified using the measured plastic gamma spectra. Furthermore, a performance indicator, namely the minimum required count for single isotopes, is defined using the mean absolute percentage error with a criterion of 1% as the metric to verify the pseudo-gamma spectroscopy performance. The obtained results confirm that the proposed model successfully unfolds the full-energy peaks and predicts the relative radioactivity, even in spectra with statistical uncertainties. View Full-Text
Keywords: plastic gamma spectrum; photopeak; full-energy peak unfolding; relative radioactivity prediction; deep learning; multitask model plastic gamma spectrum; photopeak; full-energy peak unfolding; relative radioactivity prediction; deep learning; multitask model
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MDPI and ACS Style

Jeon, B.; Kim, J.; Lee, E.; Moon, M.; Cho, G. Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning. Sensors 2021, 21, 684. https://doi.org/10.3390/s21030684

AMA Style

Jeon B, Kim J, Lee E, Moon M, Cho G. Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning. Sensors. 2021; 21(3):684. https://doi.org/10.3390/s21030684

Chicago/Turabian Style

Jeon, Byoungil; Kim, Junha; Lee, Eunjoong; Moon, Myungkook; Cho, Gyuseong. 2021. "Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning" Sensors 21, no. 3: 684. https://doi.org/10.3390/s21030684

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