Blind source extraction of HPGe preamplifier's output signals using the thinICA algorithm: detection and identification of gamma ray emitters

Abstract

In this study, the thin independent component analysis algorithm is used to solve the blind source extraction problem in the case where the observed mixtures are defined as the HPGe preamplifier's output signals. These last correspond to the response of the detector to a combination of gamma radiation emitters having different levels of radioactivity. Indeed, on the basis of the performance index values, we conclude that this algorithm is the best blind source extraction method to analyze our data. Once the separation task is achieved, we evaluate the signal to noise ratio from individual columns of the mixing matrix. The values of this parameter permit us to detect easily the number of radionuclides used in the  Corresponding author 1158 Abdelhamid Mekaoui et al.experiment. Also, we calculate and plot the correlation functions between the signals recorded using one radioactive element and the extracted independent components. The interpretation of the gotten graphics allows us to associate each estimated independent component to the appropriate gamma radiation emitter.

Neutron Flux Monitoring Based on Blind Source Separation Algorithms in Moroccan TRIGA MARK II Reactor

“…The application of new digital signal processing methods, the so-called Blind Source Separation (BSS), on nuclear data was introduced by Mekaoui et al [7][8][9]. It was used, as mentioned above, for the detection and identification of gamma radiation emitters and for solving the pileup problem at the output of HPGe preamplifier used in gamma spectrometry chains.…”

“…Recently, Mekaoui et al have used these methods for gamma spectrometry data processing [7]. Thus, their work aims at detecting and identifying gamma-emitters using only the signals recorded at the HPGe preamplifier's output signals [7,8]. Also, they tried to solve the pileup problem at the HPGe output [7,9].…”