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The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

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Nuclear Science and Techniques 2023年第4期34卷 149-160页

作者： Hong-Fei Xiao Qing-Xian Zhang He-Yi Tan Bin Shi Jun Chen Zhi-Qiang Cheng Jian Zhang Rui Yang Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu University of TechnologyChengdu 610059China National Key Laboratory for Metrology and Calibration Techniques China Institute of Atomic EnergyBeijing 102413China

The neutron spectrum unfolding by bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron *** with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

关键词： Particle swarm optimization Maximum likelihood expectation maximization Neutron spectrum unfolding bonner spheres spectrometer Monte Carlo method

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