Reducing noise in gamma-ray spectrometry using spectral component analysis

Abstract

Statistical methods for removing noise from multichannel spectra are now routinely applied in gamma-ray spectrometry. The two methods in common use are the Noise Adjusted Singular Value Decomposition (NASVD) method and the Maximum Noise Fraction (MNF) method. These methods use a principal component (PC) type analysis to extract the dominant spectral shapes from a dataset. These PCs are used to reconstruct spectra that have most of the original signal, but little of the noise. The NASVD and MNF methods differ mainly in how they normalise the input spectra for noise before spectral component analysis. The purpose of this paper is to evaluate these methods in terms of both the accuracy and precision of the resultant noise-reduced spectra. We develop a methodology based on the use of a synthetic spectra dataset where the true spectrum channel count rates (in the absence of noise) are known. Because the true values are known, by repeatedly processing the same synthetic data using different synthesised noise we derive estimates of both the precision and accuracy of the processed data. Our tests using synthetic data show that the NASVD and MNF methods produce almost identical results. This suggests that the differences in the way the methods normalise for noise are not of great significance. However, the MNF method produces results that are fractionally better in terms of both precision and accuracy. This may be a function of the robustness of the numerical algorithms we used to implement these methods.