利用人工神经网络对塑料闪烁体的伽马射线能谱展开。
Gamma-ray energy spectrum unfolding of plastic scintillators using artificial neural network.
机构信息
Department of Physics, School of Science, Ferdowsi University of Mashhad, 91775-1436, Mashhad, Iran.
Department of Physics, School of Science, Ferdowsi University of Mashhad, 91775-1436, Mashhad, Iran.
出版信息
Appl Radiat Isot. 2022 Aug;186:110265. doi: 10.1016/j.apradiso.2022.110265. Epub 2022 May 5.
In this study, the unfolding of the plastic scintillator spectrum was undertaken using the artificial neural networks tools of MATLAB. To this purpose, the response matrix of the plastic scintillator was generated for 145 energy groups and in 512 pulse-height channels using the MCNPX2.6 code. The results confirmed that the relative error in the gamma-ray energy unfolding with artificial neural networks is less than 3.8%.
在这项研究中，使用 MATLAB 的人工神经网络工具展开了塑料闪烁体的能谱。为此，使用 MCNPX2.6 代码为 145 个能群和 512 个脉冲高度通道生成了塑料闪烁体的响应矩阵。结果证实，使用人工神经网络进行伽马射线能量展开的相对误差小于 3.8%。
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