Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2012, Vol. 23 ›› Issue (6): 332-336 doi: 10.13538/j.1001-8042/nst.23.332-336


Information retrieval methods for high resolution γ-ray spectra

WU Hexi~(1,2,*) ZHANG Huaiqiang~2 LIU Qingcheng~(1,2,*) YANG Bo~2 WEI Qianglin~2 YUAN Xinyu~2   

  1. 1 Engineering Research Center of Nuclear Technology Application(East China Institute of Technology,Fuzhou) Ministry of Education, Nanchang 330013,China 2 School of Nuclear Engineering and Geophysics,East China Institute of Technology,Fuzhou 344000,China
  • Supported by:

    Supported by National Natural Science Foundation of China(No.41174089 and 41164003);Open-ended Foundation(No.HJSJYB2010-07) from the Chinese Engineering Research Center

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WU Hexi, ZHANG Huaiqiang, LIU Qingcheng, YANG Bo, WEI Qianglin, YUAN Xinyu, . Information retrieval methods for high resolution γ-ray spectra.Nuclear Science and Techniques, 2012, 23(6): 332-336     doi: 10.13538/j.1001-8042/nst.23.332-336


A program based on MATLAB 7.0 platform was developed to locate characteristic peak position and calculate net area of characteristic peak.The formula for the calculation of relative standard deviation of net peak area by Sterlinski's method was found excellent in searching single peaks and resolving overlapping peaks in high resolution gamma-ray spectrum.Gaussian function fitting method using Levenberg-Marquardt algorithm was applied to calculate net area of peaks.A standard test spectrum supplied by the IAEA in 1995 was analyzed by the program and another two widely used commercial software.The analysis results show the program was superior to the latter two in searching single peaks and resolving overlapping peaks.The optimized fitting indexes are found between 0.962 and 0.996,which shows that the program adopted is feasible and accurate for extracting the net peak area in high resolution gamma-ray spectra.

Key words: High resolution, Peak searching, Gauss fitting, Levenberg-Marquardt algorithm