Analysis of complex gamma-ray spectra using particle swarm optimization.

Abstract Analysis of gamma-ray spectra is an important step for identification and quantification of radionuclides in a sample. In this paper a new gamma-ray spectra analysis algorithm based on Particle Swarm Optimization (PSO) is developed to identify different isotopes of a mixed gamma-ray source and determine their fractional abundances. PSO is an iterative algorithm that imitates the social behaviors observed in nature to solve complex optimization problems. The PSO method is used for complex fitting to the response of a 3 × 3 inch NaI (Tl) scintillation detector and the fitting process is controlled by a test for significance of abundance and computation of Theil coefficient. To test the developed algorithm, a number of experimentally measured gamma-ray spectra related to a mixed gamma-ray source including different combinations of 60 Co , 137 Cs , 22 Na , 152 Eu and 241 Am isotopes are analyzed using information of whole spectrum. The performance of the developed PSO algorithm is compared to the multiple linear regression (MLR) method as well. The results of the developed PSO algorithm show a better match with the real fractional abundances than that of MLR method. Highlights • Introducing a novel method for gamma-ray spectrum analysis. • Whole spectrum analysis using particle swarm optimization (PSO). • Calculation of fractional abundances of isotopes of mixed gamma-ray sources. • Comparing results of the PSO method with those of multiple linear regression (MLR). • Better results of the PSO method in comparison with MLR method. [ABSTRACT FROM AUTHOR]