Online spectral unmixing in gamma-ray spectrometry.

We introduced in a previous paper a time-dependent full-spectrum analysis algorithm speeding up the estimation of the activity of the radionuclides present in a sample. In this paper, we present a new version of the algorithm allowing online estimation. It uses only on a buffer of few segments while keeping the time information by using a time dependent regularization, thus reducing the size of the data matrices and the length of the processing of each iteration.

Analysis of gamma-ray spectra with spectral unmixing - Part I: Determination of the characteristic limits (decision threshold and statistical uncertainty) for measurements of environmental aerosol filters.

Poisson-statistics based spectral unmixing has been shown to be an efficient analysis tool for the radionuclides activity estimation from gamma-ray spectrometry measurements. However, the calculation of the corresponding characteristic limits has not been investigated so far. In this paper, we present the quantification of the decision threshold and the limits of the coverage interval for the metrological use of such spectral unmixing algorithms.

Analysis of gamma-ray spectra with spectral unmixing, Part II: Recalibration for the quantitative analysis of HPGe measurements.

In the context of radioactivity measurements, the quantitative analysis of a gamma-ray spectrum depends on the analysis algorithm. To that end, we recently introduced a Poisson statistics-based spectral unmixing approach. However, it also relies on a proper instrument recalibration as well as on an uncertainty estimation, for which no solution has been proposed so far.

Sparse BSS from Poisson Measurements.

The problem of sparse Blind Source Separation (BSS) has been extensively studied when the noise is additive and Gaussian. This is however not the case when the measurements follow Poisson or shot noise statistics, which is customary with counting-based measurements. To that purpose, we introduce a novel sparse BSS algorithm coined pGMCA (poisson-Generalized Morphological Component Analysis) that specifically tackles the blind separation of sparse sources from measurements following Poisson statistics.

Spectral unmixing applied to fast identification of γ-emitting radionuclides using NaI(Tl) detectors.

Spectral unmixing was investigated for fast spectroscopic identification in γ-emitter mixtures at low-statistics in the case of measurements performed to prevent illegal nuclear material trafficking or for in situ environmental analysis following a radiological or nuclear accident. For that purpose, a multiplicative update algorithm based on full-spectrum analysis was tested in the case of a 3″x3″ NaI(Tl) detector. Automatic decision-making was addressed using Monte Carlo calculations of decision thresholds and detection limits.