The combined application of principal component analysis and decision tree in nuclear pulse shape discrimination

Zhang, Zhenhua; Hu, Chuangye; Zhang, Yifei; Liao, Bin; Zhu, Jiajun; Fan, Xinyang; Li, Yutong; Zhang, Linqi

doi:10.1016/j.nima.2019.162425

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…Integral ratio methods such as charge integral (CI) and partial charge-to-peak ratio (PCPR) methods are typical [20]. And machine learning methods include decision trees [21], support vector machines [22], K-nearest neighbors [23], artificial neural networks [24], and other approaches. The False Alarm Rate (FAR) values obtained using gamma sources can be utilized to evaluate the accuracy of the methods as mentioned above [20,25].…”

Section: Algorithms Of Pulse Shape Discriminationmentioning

confidence: 99%

A low-power neutron and gamma-ray detector for environmental radiation monitoring using CLYC scintillator

Zhao,

Huang,

Shan

et al. 2023

J. Inst.

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This paper describes a low-power neutron and gamma-ray detector based on a CLYC scintillator enriched with 96% 6Li for environmental radiation monitoring. The detector is designed in compliance with the IP66 standard having water and dust release capability. The power consumption of the detector is 3.5 W, and the operating temperature is from -15° to +40°. The energy response range for gamma rays is 40 keV to 3 MeV, while the energy resolution of the detector is 4.8% (662 keV). A standard figure of merit (FOM) is used to evaluate neutron-gamma discrimination, achieving a value of 3.42. The mixed neutron-gamma environment was simulated at the Back-n neutron beam of the China Spallation Neutron Source (CSNS). The test results show that the detector can detect the changes in the gamma dose and neutron count rates when the beam is turned on and off in real-time. The proposed detector is suitable for the application of long-term environmental radiation monitoring for nuclear facilities or buildings housing radioactive sources.

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Section: Algorithms Of Pulse Shape Discriminationmentioning

confidence: 99%

A low-power neutron and gamma-ray detector for environmental radiation monitoring using CLYC scintillator

Zhao,

Huang,

Shan

et al. 2023

J. Inst.

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“…As a typical classification problem, some machine learning (ML) methods have been introduced into this field, such as decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN) and convolutional neural network (CNN) [9][10][11][12][13][14]. Among them, CNNs possess many excellent characteristics, such as local connectivity, parameter sharing, and adaptability.…”

Section: Introductionmentioning

confidence: 99%

n/γ discrimination for CLYC detector using a one-dimensional Convolutional Neural Network

et al. 2023

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In this work, a one-dimensional convolutional neural network (1D-CNN) is used for performing pulse shape discrimination (PSD) between the neutrons and gamma rays detected by the Cs2LiYCl6: Ce3+(CLYC) crystal. We use three different optimizers to train the CNN for comparing the effects of different optimizers on the training results. The neural network that uses the RMSProp optimizer performed the best. The accuracy of the AmBe source reaches 99.395%, and the false alarm rates (FARs) of the gamma source, i.e., 137Cs and 22Na are only 0.003% and 0.020%, separately. By the same dataset, we introduced several other methods to compare, including the classic charge integral (CI), partial charge-to-peak ratio (PCPR), decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), and artificial neural network (ANN). Among these introduced methods, the FARs of the ANN method are better, which are 0.004% and 0.031%; however, its error is higher than that of the CNN method. A detailed discussion of the discrimination capability as a function of the sampling rate of the digitizer is also presented. We compare the performance of the CNN method and the traditional integral method under different sampling rates. The results show that even under a low sampling rate, the discrimination of the CNN method is almost unchanged, while the accuracy of the traditional integral method deteriorates rapidly. In addition, the CNN method is used for classifying more complicated particles, neutrons, gamma-rays, noise, and pile-up waveforms. The classification results show that the CNN has the ability to separate the four signals from the dataset efficiently.

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“…Yet in some cases, this method may response slowly. Reference [8] reported on the combined application of PCA and decision tree (DT) in PSD. Compared with SVM, this method is fast enough for most cases.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for scintillation detector based on BP neural network

Xie¹,

Yan²,

Li³

et al. 2021

J. Inst.

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This article gives a scintillation detector fault diagnosis method based on BP neural network. From the aspect of output signals of scintillation detectors, the wavelet packet transform is used to extract the energy characteristic vectors which are treated as the input of BP neural network, and a training database is established as well as BP neural network parameters are optimized. Then the method is employed to establish a fault recognition model and fault types can be concluded. Finally, the simulation data are compared with those of two other methods (the statistical diagnosis method and an method based on multi-classification support vector machine). The experimental results illustrate that the application of proposed method can improve the fault diagnosis accuracy of scintillation detectors effectively.

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The combined application of principal component analysis and decision tree in nuclear pulse shape discrimination

Cited by 11 publications

References 13 publications

A low-power neutron and gamma-ray detector for environmental radiation monitoring using CLYC scintillator

A low-power neutron and gamma-ray detector for environmental radiation monitoring using CLYC scintillator

n/γ discrimination for CLYC detector using a one-dimensional Convolutional Neural Network

Fault diagnosis method for scintillation detector based on BP neural network

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