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

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 spectrum.Compared 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%.

Fast nuclide identification based on a sequential Bayesian method

The rapid identification of radioactive substances in public areas is crucial.However,traditional nuclide identification methods only consider information regarding the full energy peaks of the gamma-ray spectrum and require long recording times,which lead to long response times.In this paper,a novel identification method using the event mode sequence(EMS)information of target radionuclides is proposed.The EMS of a target radionuclide and natural background radiation were established as two different probabilistic models and a decision function based on Bayesian inference and sequential testing was constructed.The proposed detection scheme individually processes each photon.When a photon is detected and accepted,the corresponding posterior probability distribution parameters are estimated using Bayesian inference and the decision function is updated.Then,value of the decision function is compared to preset detection thresholds to obtain a detection result.Experiments on different target radionuclides(137Cs and 60Co)were performed.The count rates of the regions of interest(ROI)in the backgrounds between[651,671],[1154,1186],and[1310,1350]keV were 2.35,5.14,and 0.57 CPS,respectively.The experimental results demonstrate that the average detection time was 6.0 s for 60Co(with an activity of 80400 Bq)at a distance of 60 cm from the detector.The average detection time was 7 s for 137Cs(with an activity of 131000 Bq)at a distance of 90 cm from the detector.The results demonstrate that the proposed method can detect radioactive substances with low activity.

Correction to:Fast nuclide identification based on a sequential Bayesian method

Correction to:NUCL SCI TECH(2021)32:143 https://doi.org/10.1007/s41365-021-00982-z In the original article,the funding information were missing and in this correction the funding information is given below:Funding This work was supported by the National Natural science Foundation of China(No.41774190),the Special-funded program on national key scientific instruments and equipment development(No.2017YFC0602105),and the Department of Science and Technology of Sichuan Province(No.2020JDRC0109).