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Proceedings Paper

Using ALISA for high-speed classification of the components and their concentrations in mixtures of radioisotopes
Author(s): David Portnoy; Peter Bock; Peter C. Heimberg; Eric T. Moore
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Paper Abstract

An ALISA Vector Module (AVM) is trained on the discrete gamma-ray emission spectra of 61 commonly occurring radioisotopes generated by an analytical model. The trained AVM is then used to decompose the spectra captured from actual sources in the field using low-resolution thallium-activated sodium-iodide (NaI) detectors and/or high-resolution high-purity germanium (HPGe) detectors using QR Factorization to find the optimal least-squares solution for an over-specified system of equations, even if inconsistent. For low-resolution NaI detectors, formal experiments conducted under carefully controlled laboratory conditions yield average classification (spectral decomposition) errors less than 6% in mixtures with up to 10 components in test samples consisting of 1,000 photonic events, which requires just a few seconds to obtain in typical situations. Preliminary experiments with the high-resolution HPGe detector yield dramatically smaller errors than with the NaI detector. Further improvements in the accuracy and precision of the training data, as well as fusion with other powerful classification methods, are expected to reduce the error without prohibitively increasing the computation time.

Paper Details

Date Published: 26 October 2004
PDF: 10 pages
Proc. SPIE 5541, Penetrating Radiation Systems and Applications VI, (26 October 2004); doi: 10.1117/12.555833
Show Author Affiliations
David Portnoy, The George Washington Univ. (United States)
Peter Bock, The George Washington Univ. (United States)
Peter C. Heimberg, Bechtel Nevada Remote Sensing Lab. (United States)
Eric T. Moore, Bechtel Nevada Remote Sensing Lab. (United States)


Published in SPIE Proceedings Vol. 5541:
Penetrating Radiation Systems and Applications VI
F. Patrick Doty; Richard C. Schirato; H. Bradford Barber; Hans Roehrig, Editor(s)

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