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Nondestructive technique for identifying nuclides using neutron resonance transmission analysis at CSNS Back-n

Nuclear Science and Techniques 2024年第1期35卷 178-187页

作者： Sheng-Da Tang Yong-Hao Chen Jing-Yu Tang Rui-Rui Fan Qiang Li Gong Li Dong Liu Zheng-Yao Jin Xing-Zhu Cui Tian-Xiang Chen Yi-Wei Yang Rong Liu Han Yi Yang Li Zhen Yang Qi An Hao-Fan Bai Jiang-Bo Bai Jie Bao Ping Cao Qi-Ping Chen Zhen Chen Zeng-Qi Cui An-Chuan Fan Chang-Qing Feng Fan-Zhen Feng Ke-Qing Gao Min-Hao Gu Chang-Cai Han Zi-Jie Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Han-Xiong Huang Wei-Hua Jia Hao-yu Jiang Wei Jiang Zhi-jie Jiang Ling Kang Bo Li Chao Li Jia-Wen Li Xiao Li Jie Liu Shu-Bin Liu Guang-Yuan Luan Chang-Jun Ning Bin-Bin Qi Jie Ren Zhi-Zhou Ren Xi-Chao Ruan Zhao-Hui Song Kang Sun Zhi-Xin Tan Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Zhong-Wei Wen Xiao-Guang Wu Xuan Wu Li-Kun Xie Yong-Ji Yu Guo-Hui Zhang Lin-Hao Zhang Mo-Han Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Yue Zhang Zhi-Yong Zhang Mao-Yuan Zhao Lu-Ping Zhou Zhi-Hao Zhou Ke-Jun Zhu Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China Institute of High-Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China USTC Archaeometry Laboratory University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China

Nondestructive and noninvasive neutron assays are essential applications of neutron techniques.Neutron resonance transmission analysis(NRTA)is a powerful nondestructive method for investigating the elemental composition of an object.The back-streaming neutron line(Back-n)is a newly built time-of-flight facility at the China Spallation Neutron Source(CSNS)that provides neutrons in the eV to 300 MeV range.A feasibility study of the NRTA method for nuclide identification was conducted at the CSNS Back-n via two test experiments.The results demonstrate that it is feasible to identify different elements and isotopes in samples using the NRTA method at Back-n.This study reveals its potential future applications.

关键词： CSNS White neutron beam NRTA nuclide identification Nondestructive method

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Fast nuclide identification based on a sequential Bayesian method

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Nuclear Science and Techniques 2021年第12期32卷 116-127页

作者： Xiao-Zhe Li Qing-Xian Zhang He-Yi Tan Zhi-Qiang Cheng Liang-Quan Ge Guo-Qiang Zeng Wan-Chang Lai Chengdu University of Technology The College of Nuclear Technology and Automation EngineeringChengdu 610059China

The rapid identification of radioactive substances in public areas is crucial.However,traditional nuclide identification methods only consider information regarding the full energy peaks of the gamma-ray spectrum and require long recording times,which lead to long response times.In this paper,a novel identification method using the event mode sequence(EMS)information of target radionuclides is proposed.The EMS of a target radionuclide and natural background radiation were established as two different probabilistic models and a decision function based on Bayesian inference and sequential testing was constructed.The proposed detection scheme individually processes each photon.When a photon is detected and accepted,the corresponding posterior probability distribution parameters are estimated using Bayesian inference and the decision function is updated.Then,value of the decision function is compared to preset detection thresholds to obtain a detection result.Experiments on different target radionuclides(137Cs and 60Co)were performed.The count rates of the regions of interest(ROI)in the backgrounds between[651,671],[1154,1186],and[1310,1350]keV were 2.35,5.14,and 0.57 CPS,respectively.The experimental results demonstrate that the average detection time was 6.0 s for 60Co(with an activity of 80400 Bq)at a distance of 60 cm from the detector.The average detection time was 7 s for 137Cs(with an activity of 131000 Bq)at a distance of 90 cm from the detector.The results demonstrate that the proposed method can detect radioactive substances with low activity.

关键词： Natural radiation nuclide identification Sequential testing Nuclear safety

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Novel algorithm for detection and identification of radioactive materials in an urban environment

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Nuclear Science and Techniques 2023年第10期34卷 103-116页

作者： Hao-Lin Liu Hai-Bo Ji Jiang-Mei Zhang Jing Lu Cao-Lin Zhang Xing-Hua Feng Department of Automation University of Science and Technology of ChinaHefei 230026China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China Fundamental Science on Nuclear Wastes and Environment Safety Laboratory Southwest University of Science and TechnologyMianyang 621010China School of Automation and Information Engineering Sichuan University of Science and EngineeringZigong 643000China

This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance the quality and reliability of the energy spectrum data, we devised a temporal energy window. This partitioned the time-series detector response data, resulting in energy spectra that emphasize the vital information pertaining to radioactive materials. We then extracted characteristic features of these energy spectra, relying on the formation mechanism and measurement principles of the gammaray instrument spectrum. These features encompassed aggregated counts, peak-to-flat ratios, and peak-to-peak ratios. This methodology not only simplified the interpretation of the energy spectra's physical significance but also eliminated the necessity for peak searching and individual peak analyses. Given the requirements of imbalanced multi-classification, we created a detection and identification model using a weighted k-nearest neighbors(KNN) framework. This model recognized that energy spectra of identical radioactive materials exhibit minimal inter-class similarity. Consequently, it considerably boosted the classification accuracy of minority classes, enhancing the classifier's overall efficacy. We also executed a series of comparative experiments. Established methods for radionuclide identification classification, such as standard KNN, support vector machine, Bayesian network, and random tree, were used for comparison purposes. Our proposed algorithm realized an F1 measure of 0.9868 on the time-series detector response data, reflecting a minimum enhancement of 0.3% in comparison with other techniques. The results conclusively show that our algorithm outperforms others when applied to time-series detector response data in urban contexts.

关键词： Gamma-ray spectral analysis nuclide identification Urban environment Temporal energy window Peakratio spectrum analysis Weighted KNN

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