Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamm...Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal.展开更多
In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ ...In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.展开更多
Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive att...Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive attention from researchers,where real-time neutron/gamma pulse discrimination is the critical factor among detector performance parameters.This study investigated the discrimination performance of the charge comparison,amplitude comparison,time comparison,and pulse gradient_(m)ethods and the effects of a Sallen–Key filter on their performance.Experimental results show that the figure of merit(FOM)of all four methods is improved by proper filtering.Among them,the charge comparison method exhibits excellent noise resistance;moreover,it is the most_(s)uitable method of real-time discrimination for CLLB detectors.However,its discrimination performance depends on the parameters t_(s),t_(m),and t_(e).When t_(s)corresponds to the moment at which the pulse is at 10%of its peak value,t_(e)requires a delay of only 640–740 ns compared to t_(s),at which time the potentially optimal FOM of the charge comparison method at 3.1–3.3 MeV is greater than 1.46.The FOM obtained using the t_(m)value calculated by a proposed maximized discrimination difference model(MDDM)and the potentially optimal FOM differ by less than 3.9%,indicating that the model can provide good guidance for parameter selection in the charge comparison method.展开更多
Purpose Fast neutrons and gamma-ray imaging detection is an effective way to detect and identify radioactive material in the field of nuclear security.A compact coded aperture imaging(CAI)camera was designed to be sen...Purpose Fast neutrons and gamma-ray imaging detection is an effective way to detect and identify radioactive material in the field of nuclear security.A compact coded aperture imaging(CAI)camera was designed to be sensitive to both gamma and neutron radiation based on plastic scintillators and multi-pixel photon counters(MPPC).Methods MPPCs coupling with the 13×13 pixelated plastic scintillators one-to-one were utilized to reduce the scale of the CAI system while maintaining good positional performance.The symmetric charge division(SCD)circuit was adopted to reduce the 169 signals output from the MPPC array to 26.Each waveform was collected and processed with four Domino Ring Sampler 4(DRS4)chips and two 16-channel analog-to-digital converter(ADC)modules.As the pulse shapes of fast neutrons would be broadened after elastic scattering multiple times in the scintillators,the Anger-Logic method was applied to eliminate multiple elastic scattering events so that good pulse shape discrimination(PSD)performance can be achieved.Results The imaging and detection ability of the camerawas evaluated using the 241Am-Be(5.9×10^(5) n/s)neutron source and 137Cs(370 MBq)gammasource.The camera can be used to detect fast neutrons(0.5–10 MeV)and gammarays(0.2–2.5MeV).Furthermore,it can implement efficient neutron/gamma PSD capabilities in the mixed-field environment.The figure of merit(FOM)of the camera calculated at 400keVee energy cut is 0.93.Conclusion A compact MPPC-based CAI camera was designed to detect and discriminate fast neutrons and gamma rays.Its good PSD performance was well suited to distinguish fast neutrons from gamma rays in a dual-particle environment.The portable design makes it promising for complex monitoring scenarios in nuclear security.展开更多
基金supported by the Key Science and Technology projects of Leshan(No.19SZD117)the Sichuan Science and Technology Program(No.2021JDRC0108)。
文摘Neutron and gamma ray pulse signal discrimination technology is an essential part of many modern scientific fields,such as biology,geology,radiation imaging,and nuclear medicine.Neutrons are always accompanied by gamma rays due to their unique penetration characteristic;thus,the development of n-γdiscrimination methods is especially crucial.In the present study,a novel n-γdiscrimination method is proposed that implements a pulse-coupled neural network for n-γdiscrimination.In addition,experiments were conducted on the pulse signals detected by an EJ299-33 plastic scintillator,which is especially suitable for n-γdiscrimination.The proposed method was compared to three other discrimination methods,including the back-propagation neural network(BPNN),the fractal spectrum method,and the charge comparison method,with respect to two aspects:(i)the figure of merit(FoM)and(ii)discrimination time.The experimental results showed that the pulse-coupled neural network(PCNN)has a 26.49%improvement in FoM-value compared to the charge comparison method,a72.80%improvement compared to the BPNN,a 66.24%improvement compared to the fractal spectrum method,and the second-fastest discrimination time of 2.22 s.In conclusion,the PCNN treats the input signal as a whole for analysis and processing,imparting it with an excellent antinoise effect and the ability to process the dynamic information contained in a pulse signal.
基金Supported by National Natural Science Foundation of China(11275134,11475117)
文摘In this work, a new neutron and γ （n/γ） discrimination method based on an Elman Neural Network （ENN） is proposed to improve the discrimination performance of liquid scintillator （LS） detectors. Neutron and γ data were acquired from an EJ-335 LS detector, which was exposed in a 241Am-9Be radiation field. Neutron and γ events were discriminated using two methods of artificial neural network including the ENN and a typical Back Propagation Neural Network （BPNN） as a control. The results show that the two methods have different n/γ discrimination performances. Compared to the BPNN, the ENN provides an improved of Figure of Merit （FOM） in n/γ discrimination. The FOM increases from 0.907 4- 0.034 to 0.953 4- 0.037 by using the new method of the ENN. The proposed n/γdiscrimination method based on ENN provides a new choice of pulse shape discrimination in neutron detection.
基金supported by cooperation projects between an enterprise(CNPE)and a research institute(ASIPP)(Y15HX16706).
文摘Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive attention from researchers,where real-time neutron/gamma pulse discrimination is the critical factor among detector performance parameters.This study investigated the discrimination performance of the charge comparison,amplitude comparison,time comparison,and pulse gradient_(m)ethods and the effects of a Sallen–Key filter on their performance.Experimental results show that the figure of merit(FOM)of all four methods is improved by proper filtering.Among them,the charge comparison method exhibits excellent noise resistance;moreover,it is the most_(s)uitable method of real-time discrimination for CLLB detectors.However,its discrimination performance depends on the parameters t_(s),t_(m),and t_(e).When t_(s)corresponds to the moment at which the pulse is at 10%of its peak value,t_(e)requires a delay of only 640–740 ns compared to t_(s),at which time the potentially optimal FOM of the charge comparison method at 3.1–3.3 MeV is greater than 1.46.The FOM obtained using the t_(m)value calculated by a proposed maximized discrimination difference model(MDDM)and the potentially optimal FOM differ by less than 3.9%,indicating that the model can provide good guidance for parameter selection in the charge comparison method.
基金the MajorDeployment Projects of Chinese Academy of Sciences(Grant Number ZDRWCN-2018-1-01)the Research Equipment Development Project of Chinese Academy of Sciences(Grant Number YZ201415).
文摘Purpose Fast neutrons and gamma-ray imaging detection is an effective way to detect and identify radioactive material in the field of nuclear security.A compact coded aperture imaging(CAI)camera was designed to be sensitive to both gamma and neutron radiation based on plastic scintillators and multi-pixel photon counters(MPPC).Methods MPPCs coupling with the 13×13 pixelated plastic scintillators one-to-one were utilized to reduce the scale of the CAI system while maintaining good positional performance.The symmetric charge division(SCD)circuit was adopted to reduce the 169 signals output from the MPPC array to 26.Each waveform was collected and processed with four Domino Ring Sampler 4(DRS4)chips and two 16-channel analog-to-digital converter(ADC)modules.As the pulse shapes of fast neutrons would be broadened after elastic scattering multiple times in the scintillators,the Anger-Logic method was applied to eliminate multiple elastic scattering events so that good pulse shape discrimination(PSD)performance can be achieved.Results The imaging and detection ability of the camerawas evaluated using the 241Am-Be(5.9×10^(5) n/s)neutron source and 137Cs(370 MBq)gammasource.The camera can be used to detect fast neutrons(0.5–10 MeV)and gammarays(0.2–2.5MeV).Furthermore,it can implement efficient neutron/gamma PSD capabilities in the mixed-field environment.The figure of merit(FOM)of the camera calculated at 400keVee energy cut is 0.93.Conclusion A compact MPPC-based CAI camera was designed to detect and discriminate fast neutrons and gamma rays.Its good PSD performance was well suited to distinguish fast neutrons from gamma rays in a dual-particle environment.The portable design makes it promising for complex monitoring scenarios in nuclear security.
