Spectral methods for reducing noise in gamma-ray spectrometry

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 Maximum Noise Fraction (MNF) method and the Noise Adjusted Singular Value Decomposition (NASVD) method. These methods use a principal component (PC) type analysis to extract the dominant spectral shapes from a dataset. These PCs are then used to construct smooth spectra. The purpose of this paper is to evaluate current spectral smoothing methods in terms of both their accuracy and precision. 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. By repeatedly processing the same synthetic data using different synthesised noise, and because the true values of the synthetic data are known, we derive estimates of both the precision and accuracy of the processed data. Our tests using synthetic data show that the MNF and NASVD methods produce almost identical results. 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.