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A new physics-based method for detecting weak nuclear signals via spectral decomposition
Journal article   Peer reviewed

A new physics-based method for detecting weak nuclear signals via spectral decomposition

Kung-Sik Chan, Jinzheng Li, William Eichinger and Erwei Bai
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, Vol.667, pp.16-25
2012
DOI: 10.1016/j.nima.2011.11.067

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Abstract

We propose a new physics-based method to determine the presence of the spectral signature of one or more nuclides from a poorly resolved spectra with weak signatures. The method is different from traditional methods that rely primarily on peak finding algorithms. The new approach considers each of the signatures in the library to be a linear combination of subspectra. These subspectra are obtained by assuming a signature consisting of just one of the unique gamma rays emitted by the nuclei. We propose a Poisson regression model for deducing which nuclei are present in the observed spectrum. In recognition that a radiation source generally comprises few nuclear materials, the underlying Poisson model is sparse, i.e. most of the regression coefficients are zero (positive coefficients correspond to the presence of nuclear materials). We develop an iterative algorithm for a penalized likelihood estimation that prompts sparsity. We illustrate the efficacy of the proposed method by simulations using a variety of poorly resolved, low signal-to-noise ratio (SNR) situations, which show that the proposed approach enjoys excellent empirical performance even with SNR as low as to −15 db.
 Information criteria Gamma-ray signature recognition Sparsity Penalized likelihood Poisson regression

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