Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer  

作　　者：Chun-Di Fan Guo-Qiang Zeng Hao-Wen Deng Lei Yan Jian Yang Chuan-Hao Hu Song Qing Yang Hou 

机构地区：[1]College of Nuclear Technology and Automation Engineering,Chengdu University of Technology,Chengdu 610059,China

出　　处：《Nuclear Science and Techniques》2024年第2期64-84,共21页核技术（英文）

基　　金：This work was supported by the National Key R&D Program of China(Nos.2022YFF0709503,2022YFB1902700,2017YFC0602101);the Key Research and Development Program of Sichuan province(No.2023YFG0347);the Key Research and Development Program of Sichuan province(No.2020ZDZX0007).

摘　　要：To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

关 键 词：High-purity germaniumγ-ray spectrometer Pulse-shape discrimination Compton scattering Artificial neural network Minimum detectable activity 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TL817.2[自动化与计算机技术—控制科学与工程]