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    • Application of Neural Networks for the Analysis of Gamma-Ray Spectra Measured with a Ge Spectrometer

    E Yoshida,K Shizuma,S Endo,T Oka

    Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detector...（2002）

    hiroshima university | Hiroshima Univ

    Cited 102|Views8
    Abstract
    The analysis of gamma-ray spectra to identify lines and their intensities usually requires expert knowledge and time-consuming calculations with complex fitting functions. A neural network algorithm can be applied to a gamma-ray spectral analysis owing to its excellent pattern recognition characteristics. However, a gamma-ray spectrum typically having 4096 channels is too large as a typical input data size for a neural network. We show that by applying a suitable peak search procedure, gamma-ray data can be reduced to peak energy data, which can be easily managed as input by neural networks. The method was applied to the analysis of gamma-ray spectra composed of mixed radioisotopes and the spectra of uranium ores. Radioisotope identification was successfully achieved.
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    gamma-ray spectrometry,neural network,radioisotope identification
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    【要点】：本文提出了一种基于神经网络算法分析伽马射线光谱的新方法，通过将伽马射线数据简化为峰能量数据，有效实现了混合放射性同位素和铀矿光谱的分析与识别。
    

    【方法】：作者将伽马射线光谱数据通过合适的峰搜索程序转化为峰能量数据，利用神经网络强大的模式识别能力进行分析。
    

    【实验】：该研究将方法应用于包含混合放射性同位素的伽马射线光谱分析，并成功识别出放射性同位素，实验使用的数据集为实际测量的伽马射线光谱数据，并获得了满意的结果。