Optimization of pulse processing parameters for digital neutron-gamma discrimination

Lotfi, Y.; Moussavi-Zarandi, S.A.; Ghal-Eh, Nima; Bayat, E.

doi:10.1016/j.radphyschem.2019.108346

Cited by 9 publications

(5 citation statements)

References 12 publications

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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%

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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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Section: Experimental Setups and Parameter Settingsmentioning

confidence: 99%

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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“…In the vector projection (VP) method [26,27], the pulse signals of different particles are considered as vectors pointing in different directions in a vector space. First, n-c PSs were normalized.…”

Section: Vector Projectionmentioning

confidence: 99%

“…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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“…For accurate neutron measurements in high X-ray flux conditions, it is necessary to optimize the detector system to increase the FoM value. We determined the optimal PSD parameters, which are pulse width and delay time, to increase the FoM index [14].…”

Section: Optimizationmentioning

confidence: 99%

Optimization of fast neutron and X-ray pulse shape discrimination (PSD) using an EJ276G scintillator in high radiation mixed conditions

et al. 2022

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Organic crystalline, liquid, and plastic scintillators with low Z-number materials are commonly used in fast neutron detectors. Pulse shape discrimination (PSD) is used to distinguish neutrons from X-rays because these detectors are affected by X-rays due to Compton scattering during fast neutron measurement. PSD was performed with a 1 × 1 × 3 cm3 EJ276G plastic scintillator, SiPM, and DAQ (10-bit resolution, 400 MHz) using the charge comparison method, which compares the total charge (Q body) and the delayed charge (Q tail) from the peak. Most studies have been conducted under laboratory conditions, such as with 252Cf or 241Am-Be sources. This study will perform PSD in a 15 MeV electron accelerator with a total flux of 6.72 × 1014 #/sec and an X-ray-to-neutron ratio of up to 4071:1. The PSD was optimized before the measurement in high flux conditions because it was difficult to perform PSD due to the pile-up effect in these conditions. Optimization focused on the figure of merit (FoM), which is an index of PSD performance. To distinguish neutrons from X-rays under these conditions, lead shielding is necessary to reduce the X-ray-to-neutron ratio. We performed Monte Carlo N-Particle Transport Code (MCNP6) simulations to determine the optimal shielding thickness. Fast neutrons interact with lead due to its high density, which results in small neutron energy loss and direction changes by scattering. This effect can affect other pixels as noise. We plan to evaluate the neutron images under high X-ray flux conditions by comparing the images of the MCNP6 simulation results and the measurement results.

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Optimization of pulse processing parameters for digital neutron-gamma discrimination

Cited by 9 publications

References 12 publications

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

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

Optimization of fast neutron and X-ray pulse shape discrimination (PSD) using an EJ276G scintillator in high radiation mixed conditions

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