SGSD: A novel Sequential Gamma-ray Spectrum Deconvolution algorithm

摘要

A novel approach for analyzing complex gamma-ray spectra using a sequential algorithm is introduced. The developed Sequential Gamma-ray Spectrum Deconvolution (SGSD) algorithm produces a sequence of spectra converging to the best estimation of output spectrum of a gamma-ray detector. In each point of sequence, an isotope of unknown gamma-ray source is identified and the respective response of the detector to unknown source is reconstructed. Effectiveness of the developed algorithm is demonstrated by two empirical and simulation studies. In the case of empirical study, a number of recorded gamma-ray spectra related to a mixed gamma-ray source including different combinations of 5 isotopes (Co-60, Cs-137, Na-22, Eu-152 and Am-241) are analyzed using whole information of spectra. Furthermore, a number of simulated gamma-ray spectra related to a mixed gamma-ray source including different combinations of 30 isotopes are analyzed in simulation study. Both man-made and natural radioisotopes like Ba-133, Co-60, Ir-192, Cs-137, K-40, Th-232 series, U-238 series, Ac-227 series, etc. are used for Monte Carlo simulations. The numerical results of the SGSD algorithm are compared with those of the conventional Non-Negative Least Squares (NNLS) algorithm. Based on the results, the identification procedure of the SGSD algorithm has a remarkable superiority over the NNLS algorithm. (C) 2019 Elsevier Ltd. All rights reserved.