In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

在这项研究中，使用塑料闪烁光纤、两个光子计数器并通过机器学习算法的数据处理来估计一维伽马射线源位置。使用非线性回归算法构建用于放射源位置估计的机器学习模型。使用机器学习的放射源位置估计结果与基于相同测量数据的理论位置估计结果进行比较。在源位置进行各种测试以确定源位置估计精度的改进。此外，还进行了评估以比较在改变训练数据集数量时准确度的变化。