New discrimination algorithm for artificial γ radiation sources based on average energy deposition in plastic scintillators

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 2021-09, Vol.1010, p.165573, Article 165573
• Main Authors: Hu, Bin, Ye, Hao, Li, Fu-Long, Tian, Xing Yu, Song, Le-Tian, Zhang, Yan, Zhang, Xiong-Jie, Wang, Ren-Bo, Tang, Bin
• Format: Article
• Language: English
• Subjects: Artificial radiation sources; Average energy algorithm; NORM; Plastic scintillator
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2021.165573

Plastic scintillators are characterized with high detection efficiency, high response speeds, uneven optical efficiency distributions, and poor energy resolution. In this work, we developed a new discrimination algorithm, namely, the average energy (AE) algorithm, for artificial γ radiation sources and implemented it in a hand-held plastic scintillator detector. The algorithm provided excellent discrimination capability of naturally occurring radiation materials (NORM) and artificial radiation Sources. The energy deposited by all radiation sources in small plastic scintillators, was simulated and analyzed using the Monte Carlo method. And all those radiation sources are categorized by the International Atomic Energy Agency. A plastic scintillator detector system was then constructed for proof of concept using 241Am, 137Cs, 60Co, radium-bearing ore, and thorium ore. A large-sized plastic scintillator detector was used for comparison, and the correctness of the AE algorithm was verified. The AEs of the NORM region ranged from 631.5 ± 13.3 keV to 783.6 ± 17.2 keV and 1173.6 keV or more, and the collection time of a single measurement was less than 3 s (5214 photons). Results showed that the AE algorithm could quickly and accurately identify γ artificial radiation sources from the background and NORM. The AE algorithm should always be used in conjunction with the energy window algorithm to avoid misjudgments.