Jeon Byoungil, Kim Junha, Lee Eunjoong, Moon Myungkook, Cho Gyuseong
Applied Artificial Intelligence Laboratory, Korea Atomic Energy Research Institute, Daejeon 34507, Korea.
Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.
Sensors (Basel). 2021 Jan 20;21(3):684. doi: 10.3390/s21030684.
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.
尽管塑料闪烁探测器的光谱特性较差，但它们在辐射测量的各个领域中都有广泛应用。已经提出了几种方法来促进塑料闪烁探测器在光谱测量中的应用。然而，这些探测器中的大多数只能用于识别放射性同位素。在本研究中，我们提出了一种基于塑料闪烁探测器的伪伽马能谱多任务模型。使用多任务学习实现深度学习模型，并通过监督学习进行训练。使用八个伽马射线源生成数据集。基于伽马射线源信息，使用蒙特卡罗N粒子输运程序（MCNP 6.2）模拟能谱，并使用聚乙烯甲苯探测器进行测量以生成数据集。使用随机采样技术生成单伽马射线源和多伽马射线源的能谱，并将其用作所提出模型的训练数据集。使用贝叶斯优化方法对生成的数据集调整模型的超参数。为了提高深度学习模型的性能，提出了一种具有加权多头自注意力的深度学习模块，并将其用于伪伽马能谱模型。使用测量的塑料伽马能谱验证该模型的性能。此外，使用平均绝对百分比误差定义了一个性能指标，即以1%为标准的单同位素所需最小计数，以验证伪伽马能谱性能。获得的结果证实，即使在存在统计不确定性的能谱中，所提出的模型也能成功展开全能峰并预测相对放射性。
