Study of n-γ discrimination in low energy range(above 40 keVee) by charge comparison method with a BC501A liquid scintillation detector

A VME-based experiment system for n-y discrimination using the charge comparison method was established.A data acquisition program for controlling the programmable modules and processing data online via VME64X bus was developed through the use of Lab VIEW.The two-dimensional（2D） scatter plots of the charge in the slow component vs.the total charge from ^（241）Am-Be and 252Cf neutron sources are presented.The 2D scatter plots of the energy vs.the ratio of the charge in the slow component to the total charge of the pulses are also presented.The quality of n-γ discrimination was checked by the figure-of-merit,and the results showed good performance of n-γ discrimination at the low energy range.Neutrons and γ-rays were separated above 50 keVee（electron-equivalent energy）.The quality of n-γ discrimination has been improved compared with others＇ results at five energies（150,250,350,450,550 keVee）.

CsI-bowl:an ancillary detector for exit channel selection inγ-ray spectroscopy experiments

A particle detector array designed for light-charged particles, known as the CsI-bowl, was built for exit channel selection for in-beam γ-ray spectroscopy experiments. This device is composed of 64 CsI(Tl) detectors, organized in a structure reminiscent of a tea-bowl. High quantum efficiency photodiodes, characterized by their minimal mass, were employed to collect scintillation light. Its design, construction, particle identification resolution, and its effectiveness in relation to exit channel selection are described in this paper. In source tests, the optimal figure of merit for the identification of α-particles and γ-rays using the charge comparison method was found to be 3.3 and 12.1 for CsI detectors coupled to photodiodes and avalanche photodiodes, respectively. The CsI-bowl demonstrated effectiveness in identifying particles, specifically the emission of protons and α-particles in the58Ni(19F, xpyn) fusion–evaporation reaction, thereby enabling the selection of the desired exit channels.

Performance of real‑time neutron/gamma discrimination methods

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.

Discrimination of neutrons and gamma rays in plastic scintillator based on pulse-coupled neural network

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.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

Adaptability of n-γdiscrimination and filtering methods based on plastic scintillation

Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by the detection of neutrons.The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system.The pulse signals measured by plastic scintillators contain noise,which decreases the accuracy of n-y discrimination.To improve the performance of n-y discrimination,the noise of the pulse signals should be filtered before the n-y discrimination process.In this study,the influences of the Fourier transform,wavelet transform,moving-average filter,and Kalman algorithm on the charge comparison method,fractal spectrum method,and back-propagation neural network methods were studied.It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others,with an increasing accuracy of 6.87%compared to that without the filtering process.Meanwhile,the Kalman filter offers an improvement of 3.04%over the fractal spectrum method,and the adaptability of the moving-average filter in backpropagation neural network discrimination is better than that in other methods,with an increase in 8.48%.The Kalman filtering algorithm has a significant impact on the peak value of the pulse,reaching 4.49%,and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.

Digital pulse shape discrimination methods for n-γ separation in an EJ-301 liquid scintillation detector

A digital pulse shape discrimination system based on a programmable module NI-5772 has been established and tested with an EJ-301 liquid scintillation detector. The module was operated by running programs developed in Lab VIEW, with a sampling frequency up to 1.6 GS/s. Standard gamma sources ^22 Na,^137Cs and ^60 Co were used to calibrate the EJ-301 liquid scintillation detector, and the gamma response function was obtained. Digital algorithms for the charge comparison method and zero-crossing method have been developed. The experimental results show that both digital signal processing（DSP） algorithms can discriminate neutrons from γ-rays. Moreover,the zero-crossing method shows better n-γ discrimination at 80 ke Vee and lower, whereas the charge comparison method gives better results at higher thresholds. In addition, the figure-of-merit（FOM） for detectors of two different dimensions were extracted at 9 energy thresholds, and it was found that the smaller detector presented better n-γseparation for fission neutrons.