A New Physics-Based Method to Detect Weak Nuclear Signals via Spectral Decomposition

• Activity Number: 339
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Jinzheng Li*+ and Kung-Sik Chan and William Eichinger and Erwei Bai
• Companies: University of Iowa and University of Iowa and University of Iowa and University of Iowa
• Keywords: gamma-ray spectrum ; penalized likelihood estimation ; Poisson regression ; sparsity ; weak signal detection ; radionuclide detection

Abstract

Detection of special nuclear materials hidden in cargo containers is a major national security challenge. 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, which are obtained by assuming a signature consists of just one of the unique gamma rays emitted by the nuclei. We propose a Poisson regression model to deduce which nuclei are present in the observed spectrum. We develop an iterative algorithm for a penalized likelihood estimation that provides a sparse solution. 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 -15db. The proposed method is shown to be variable-selection consistent in the framework of increasing detection time and under mild regularity conditions.