A method for correcting characteristic X-ray net peak count from drifted shadow peak

作     者：Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen Lin Tang;Xing-Ke Ma;Kai-Bo Shi;Yeng-Chai Soh;Hong-Tao Shen

作者机构：College of Electronic Information and Electrical EngineeringChengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear TechnologyGuangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and ApplicationAnhui UniversityHefei 230039China School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation EngineeringChengdu University of TechnologyChengdu 610059China 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2023年第34卷第11期

页      面：155-167页

核心收录：

学科分类：12[管理学] 082704[工学-辐射防护及环境保护] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0827[工学-核科学与技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Open Project of the Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology(No.NLK2022-05) Central Government Guidance Funds for Local Scientific and Technological Development,China(No.Guike ZY22096024) Sichuan Natural Science Youth Fund Project(No.2023NSFSC1366) Open Research Fund of the National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(No.AE202209) Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(MIMS22-04) National Natural Science Youth Foundation of China(No.12305214) 

主　　题：Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector 

摘      要：To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.