Drift problems in the automatic analysis of gamma-ray spectra using associative memory algorithms

Perturbations affecting nuclear radiation spectrometers during their operation frequently spoil the accuracy of automatic analysis methods. One of the problems usually found in practice refers to fluctuations in the spectrum gain and zero, produced by drifts in the detector and nuclear electronics. The pattern acquired in these conditions may be significantly different from that expected with stable instrumentation, thus complicating the identification and quantification of the radionuclides present in it. The performance of Associative Memory algorithms when dealing with spectra affected by drifts is explored considering a linear energy-calibration function. The formulation of the extended algorithm, constructed to quantify the possible presence of drifts in the spectrometer, is deduced and the results obtained from its application to several practical cases are commented