Discrimination of neutrons and gamma-rays in plastic scintillator based on falling-edge percentage slope method

Zuo, Zhuo; Xiao, Yu-Long; Liu, ZhenFeng; Liu, BingQi; Yan, Yu-Cheng

doi:10.1016/j.nima.2021.165483

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Another simple method for PSD makes use of the determination of the slope for the falling edge region [13]. It is easy to compute the slope of the straight line connecting two points (one upper threshold and one lower threshold) but it is less evident how to properly select these threshold points for them to be representative of the neutron or gamma process taking place in order to maximize the discrimination capabilities.…”

Section: Of 17mentioning

confidence: 99%

Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape

Baselga,

Montbarbon

2024

Applied Sciences

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Neutron/gamma pulse shape discrimination (PSD) is essential in applications such as radiation source analysis, nuclear material detection, detection of pollutants in the soil and cultural heritage. Neutrons are accompanied by gamma-ray photons due to the interaction with the environment so neutron detectors require some techniques to differentiate them. There are several methods enabling such differentiation. In the current submission, a robust estimation of the decay shape is proposed as a new alternative. To do so, a robust estimator computed by a global optimization method is used. After presenting the theoretical background and explaining the required computations to be realized, the proposed method is tested in a publicly available large dataset. Evaluations of the figure of merit and the positive discrimination rate values are used to assess the degree of improvement attained. A computing code for the method, which is easily adaptable by users to their own datasets, is also provided.

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Section: Of 17mentioning

confidence: 99%

Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape

Baselga,

Montbarbon

2024

Applied Sciences

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“…In order to evaluate the robustness and efficiency of the HQC-SCM method, four traditional and two state-of-the-art discrimination methods were utilized to compare with the HQC-SCM, including falling edge percentage slope (FEPS) [31,32], zero-crossing (ZC) [12,13], charge comparison (CC) [33], frequency gradient analysis (FGA) [15], pulsecoupled neural network (PCNN) [19], and ladder gradient methods [21]. The parameters of these discrimination methods are set at optimized values presented in [21].…”

Section: Experimental Setups and Parameter Settingsmentioning

confidence: 99%

“…However, the ZC method's small number of bins resulted in a discrete distribution of its neutron and gamma-ray groups, posing difficulties in Gaussian fitting these groups. the HQC-SCM, including falling edge percentage slope (FEPS) [31,32], zero-crossing (ZC) [12,13], charge comparison (CC) [33], frequency gradient analysis (FGA) [15], pulse-coupled neural network (PCNN) [19], and ladder gradient methods [21]. The parameters of these discrimination methods are set at optimized values presented in [21].…”

Section: Psd Performance Of Hqc-scmmentioning

confidence: 99%

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

Liu

Yang

et al. 2023

Electronics

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The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields.

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“…The falling edge percentage slope (FEPS) method [27,28] aims to realize fast real-time n-c discrimination. To realize discrimination using this method, a region of interest (ROI) must be selected, which is located in the region with the most significant differences between n-c PSs in the falling edge area.…”

Section: Falling Edge Percentage Slopementioning

confidence: 99%

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

Liu

Zuo

et al. 2022

NUCL SCI TECH

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In this study, the anti-noise performance of a pulse-coupled neural network (PCNN) was investigated in the neutron and gamma-ray (n−$$\gamma$$ 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−$$\gamma$$ γ discrimination: (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 $$\mathrm{FoM}$$ FoM -value at all noise levels. Furthermore, the fluctuations in $$\mathrm{FoM}$$ 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 zero-crossing methods under extreme noise conditions. Additionally, the changing patterns and fluctuations of the $$\mathrm{FoM}$$ FoM -value 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−$$\gamma$$ γ discrimination 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.

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Discrimination of neutrons and gamma-rays in plastic scintillator based on falling-edge percentage slope method

Cited by 7 publications

References 15 publications

Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape

Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

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

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