Estimation of Gaussian overlapping nuclear pulse parameters based on a deep learning LSTM model

Ma, Xing-Ke; Hong-quan, Huang; Wang, Qiancheng; Zhao, Jing; Yang, Fei; Kai-ming, Jiang; Ding, Wei-Cheng; Zhou, Wei

doi:10.1007/s41365-019-0691-2

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…For the energy spectrum, deconvolution methods based on constrained optimization have achieved a high-resolution boost [13,14] and may represent a complementary approach to the above pulse throughput enhancing method to compensate for resolution deterioration. With the rapid development of computing power and complex model equation-solving methods and algorithms, some works have used artificial intelligence methods [15][16][17][18] to restore pileup pulses. However, the pileup effect occurs because a certain number of piled-up pulses are rejected to maintain resolution and cannot be recognized.…”

Section: Introductionmentioning

confidence: 99%

An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers

Li,

Zhou,

Zhang

et al. 2024

Energies

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(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or the criminal smuggling of nuclear materials. High count rates can lead to signal accumulation, negatively affecting the performance of gamma spectrometers, and in severe cases, even damaging the detectors. Higher pulse throughput with better energy resolution is the ultimate goal of a gamma-ray spectrometer. Traditionally, pileup pulses, which cause dead time and affect throughput, are rejected to maintain good energy resolution. (2) Method: In this paper, an ultra-throughput boost (UTB) off-line processing method was used to improve the throughput and reduce the pileup effect of the spectrometer. Firstly, by fitting the impulse signal of the detector, the response matrix was built by the functional model of a dual exponential tail convolved with the Gaussian kernel; then, a quadratic programming method based on a non-negative least squares (NNLS) algorithm was adopted to solve the constrained optimization problem for the inversion. (3) Results: Both the simulated and experimental results of the UTB method show that most of the impulses in the pulse sequence from the scintillator detector were restored to δ-like pulses, and the throughput of the UTB method for the NaI(Tl) spectrometer reached 207 kcps with a resolution of 7.71% @661.7 keV. A reduction was also seen in the high energy pileup phenomenon. (4) Conclusions: We conclude that the UTB method can restore individual and piled-up pulses to δ-like sequences, effectively boosting pulse throughput and suppressing high-energy tailing and sum peaks caused by the pileup effect at the cost of a slight loss in energy resolution.

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Section: Introductionmentioning

confidence: 99%

An Ultra-Throughput Boost Method for Gamma-Ray Spectrometers

Li,

Zhou,

Zhang

et al. 2024

Energies

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“…e detector response function was determined by the probability distribution of the input pulse and the output pulse amplitude. Several common postprocessing methods include spectral smoothing [20,21], maximum likelihood estimation [22], and maximum entropy derivation [23], all of which involve a very complex mathematical modeling process of deconvolution, large amount of calculation, and weak generality. When a new detector is used, it needs to be remodeled and analyzed [24].…”

Section: Introductionmentioning

confidence: 99%

Application of an Improved Seeds Local Averaging Algorithm in X-ray Spectrum

Tang

Zhang

Shi

et al. 2021

Mathematical Problems in Engineering

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As an element content analysis technology, X-ray fluorescence spectrometry can be used for quantitative or semiquantitative analysis of the element content in the sample, which is of great significance for mineral census and spent fuel reprocessing. Due to the limitation of the inherent energy resolution of the detector itself, the accuracy of X-ray fluorescence analysis is difficult to be greatly improved. In some applications, even if the semiconductor detector with the best energy resolution is used, the characteristic peaks of different elements cannot be completely separated. Therefore, greatly improving the energy resolution of the detection system is a hot issue in the existing research field. To solve these problems, this paper analyzes the advantages and disadvantages of the traditional MCA (multichannel analyzer) and SLA (seeds local averaging) algorithm and proposes an ISLA (improved seeds local averaging) algorithm based on mathematical statistics. In the section of theoretical derivation, the principle of ISLA algorithm is described, whose theoretical characteristics and spectral results with different parameters are derived and simulated. In the application effect evaluation, the spectrum obtained by each method is analyzed in detail. Simulation and experimental results show that the spectrum obtained by SLA algorithm has a smaller full width at half maximum than that obtained by MCA, but the seed average process in SLA algorithm also reduces its counting rate. The optimized ISLA algorithm can not only effectively reduce the full width at half maximum of the spectral line and sharpen the spectrum peak but also compensate for the loss of the count rate of SLA algorithm.

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