Comparison of theoretical and machine learning models to estimate gamma ray source positions using plastic scintillating optical fiber detector

• Published in: Nuclear engineering and technology 2021, 53(10), , pp.3431-3437
• Main Authors: Kim, Jinhong, Kim, Seunghyeon, Song, Siwon, Park, Jae Hyung, Kim, Jin Ho, Lim, Taeseob, Pyeon, Cheol Ho, Lee, Bongsoo
• Format: Article
• Language: English
• Subjects: Gamma ray detection; Machine learning; Nonlinear regression; Plastic scintillating optical fiber; Position estimation; 원자력공학
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2021.04.019

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. •Used plastic scintillating optical fiber (PSOF) to estimate gamma ray source positions.•Used Co-60 and Cs-137 gamma ray sources to obtain the experimental data.•Constructed a machine learning model to estimate the positions of gamma ray sources.•Calculated positions of gamma ray sources by theoretical method for comparison.•Position estimations using machine learning show lower errors than theoretical model.