Application of Fireworks Algorithm in Gamma-Ray Spectrum Fitting for Radioisotope Identification

Abstract

Identification of radioisotopic signature patterns in gamma-ray spectra is of paramount importance in various applications of gamma spectroscopy. Therefore, there are several active research efforts to develop accurate and precise methods to perform automated spectroscopic analysis and subsequently recognize gamma-ray signatures. In this work, the authors present a new method for radioisotope identification in gamma-ray spectra obtained with a low resolution radiation detector. The method fits the obtained spectrum with a linear combination of known template signature patterns. Coefficients of the linear combination are evaluated by computing the solution of a single objective optimization problem, whose objective is the Theil-1 inequality coefficient. Optimization of the problem is performed by the Fireworks Algorithm, which identifies a set of coefficients that minimize the Theil-1 value. The computed coefficients are statistically tested for being significantly different than zero or not, and if at least one is found to be zero then the Fireworks Algorithm is used to reiterate fitting using the non-zero templates. Fitting iterations are continued up to the point that no linear coefficients are found to be zero. The output of the method is a list that contains the radioisotopes that have been identified in the measured spectrum. The method is tested on a set of both simulated and real experimental gamma-ray spectra comprised of a variety of isotopes, and compared to a multiple linear regression fitting, and genetic algorithm Theil-1 based fitting. Results demonstrate the potentiality of the Fireworks Algorithm based method, expressed as higher accuracy and similar precision over the other two tested methodologies for radioisotope signature pattern identification in the framework of gamma-ray spectrum fitting.

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

“…Other statistical-based methods developed for isotope identification include multiple linear regression [18], sequential deconvolution [19], Bayesian statistical inference [20], and physicsbased importance weighting [21]. In addition, several methods utilize tools from artificial intelligence such as particle swarm optimization [22], fireworks algorithm [23], expert systems [24], clustering [25], fuzzy logic [11][26], fuzzy support vector regression [27], wavelet processing [28], and fuzzy-genetic hybrid approaches [29]. Despite the large volume of research, analysis of distorted signals with random count variability in search applications remains an open challenge.…”

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

“…Additionally, paper [28] attempts to use the fireworks algorithm to solve nonlinear equation problems, and the experimental results show that the proposed algorithm has an obvious advantage in solving complex nonlinear equations with a large number of variables and high coupling of variables. In paper [29], the fireworks algorithm is applied in the identification of radioisotope signature patterns in the gamma-ray spectrum. Through simulated and real world experiments for gamma-ray spectra, the results demonstrate the potentiality of the fireworks algorithm has a higher accuracy and similar precision to that of multiple linear regression fitting and the genetic algorithm for radioisotope signature pattern identification.…”