摘要
中子探测是核能开发领域的重要技术,由于中子闪烁体探测器往往会对中子和γ射线同时响应,因此有效分辨中子和γ射线是实现高精度中子探测的先决条件。为进一步提升n/γ甄别性能,本文结合脉冲形状甄别(PSD)技术和格拉姆角场(GAF)图像转换方法,将卷积神经网络(CNN)分类模型应用到n/γ甄别中。通过GAF将n/γ脉冲数据转化为二维图像,之后将其输入到CNN分类模型中达到样本辨别的目的。为验证GAF-CNN甄别的准确性,与传统CNN甄别法和电荷比较法进行了甄别效果对比。结果表明,GAF-CNN甄别法具有更低的辨别误差率和较短的处理时间,且n/γ甄别品质因子(FOM)有着数量级上的提升。同时其具备网络轻量化的特点,有助于实现CNN PSD算法的嵌入式部署,为研制高性能n/γ复合探测能谱仪提供了一种可行的PSD技术解决方案。
Neutron detection is an important technology in the field of nuclear energy development and is involved in many research and application areas,such as particle physics,material science,cosmic ray detection and even environmental monitoring,oil well detection and nuclear medicine,etc.Since neutron scintillator detectors often respond to both neutron andγ-ray,effective discrimination between neutron andγ-ray is a prerequisite for high-precision neutron detection.In order to further explore way to enhance the performance of n/γdiscrimination,this paper combined the pulse shape discrimination(PSD)technique and the Gramian angular field(GAF)image transformation method,and applied the convolutional neural network(CNN)classification model to the n/γdiscrimination work.The 239 Pu-Be neutron source and the Cs 2LiYCl 6:Ce 3+(CLYC)detector were used to set up an experimental platform for the n/γhybrid radiation field,and 20000 original pulsed one-dimensional sequence samples were acquired through the Tektronix model DPO4034 oscilloscope.In the experiment,the charge comparison method was adopted to discriminate the original samples,and the discrimination results can be used to produce the labels of the dataset used in the GAF-CNN method and for the final comparison of the discriminative performance of the various methods.Due to the excellent performance of the CLYC detector,the discrimination effect of the charge comparison method is good,which ensures that the labeling of the dataset can be produced with high accuracy,and after the best performance of the charge comparison method has been achieved through the optimization of the window,the gap between the upper limit of the performance of the traditional method and the neural network method can be clearly found.The GAF-CNN discrimination method transformed the n/γpulse data into a two-dimensional image through the GAF,after which the image was fed into the CNN classification model for sample discrimination,which transformed the n/γdiscrimination problem into a simple image binary classification problem.Since the nuclear pulse signal is a typical time series,the use of GAF can retain the time domain features of the nuclear pulse in a more complete way,and the convolution operation of the CNN can utilize the frequency domain features,so the GAF-CNN is a kind of discrimination method that can utilize the time-frequency features at the same time.In order to verify the accuracy of GAF-CNN discrimination,the discrimination effect was compared with the traditional CNN discrimination method and the charge comparison method,where the traditional CNN discrimination method refers to the method of simply collapsing the pulsed one-dimensional sample sequences into a two-dimensional matrix and inputting it to the CNN for sample identification.The results show that the GAF-CNN discrimination method has a lower discrimination error rate and shorter processing time,and the figure of merit(FOM)of n/γdiscrimination has an order of magnitude improvement.Meanwhile,it has the characteristics of network lightweight,which helps to realize the embedded deployment of convolutional neural network PSD algorithm,and provides a feasible PSD technology solution for the development of high-performance n/γcomposite detection spectrometer.
作者
黄坤翔
张江梅
王嘉麒
苏覃
HUANG Kunxiang;ZHANG Jiangmei;WANG Jiaqi;SU Qin(College of Information Engineering,Southwest University of Science and Technology,Mianyang 621000,China;Laboratory of National Defense Key Discipline of Nuclear Waste and Environmental Safety,Southwest University of Science and Technology,Mianyang 621000,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2024年第2期461-470,共10页
Atomic Energy Science and Technology
基金
国防科工局基础科研项目(JCKY2020404C004,JCKY2022404C005)
四川省自然科学基金(2022NSFSC0044)。
关键词
n/γ甄别
脉冲形状甄别
格拉姆角场
卷积神经网络
电荷比较法
n/γdiscrimination
pulse shape discrimination
Gramian angular field
convolutional neural network
charge comparison method
