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Title: Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra

Abstract

Low-resolution isotope identifiers are widely deployed for nuclear security purposes, but these detectors currently demonstrate problems in making correct identifications in many typical usage scenarios. While there are many hardware alternatives and improvements that can be made, performance on existing low resolution isotope identifiers should be able to be improved by developing new identification algorithms. We have developed a wavelet-based peak extraction algorithm and an implementation of a Bayesian classifier for automated peak-based identification. The peak extraction algorithm has been extended to compute uncertainties in the peak area calculations. To build empirical joint probability distributions of the peak areas and uncertainties, a large set of spectra were simulated in MCNP6 and processed with the wavelet-based feature extraction algorithm. Kernel density estimation was then used to create a new component of the likelihood function in the Bayesian classifier. Furthermore, identification performance is demonstrated on a variety of real low-resolution spectra, including Category I quantities of special nuclear material.

Authors:
ORCiD logo [1];  [1];  [1]
  1. Univ. of Illinois at Urbana-Champaign, Champaign, IL (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
Contributing Org.:
University of Illinois
OSTI Identifier:
1367512
Grant/Contract Number:  
NA0002534
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Nuclear Science
Additional Journal Information:
Journal Volume: 64; Journal Issue: 7; Journal ID: ISSN 0018-9499
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; detectors; isotopes; continuous wavelet transforms; uncertainty; feature extraction

Citation Formats

Stinnett, Jacob, Sullivan, Clair J., and Xiong, Hao. Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra. United States: N. p., 2017. Web. doi:10.1109/TNS.2017.2676045.
Stinnett, Jacob, Sullivan, Clair J., & Xiong, Hao. Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra. United States. doi:10.1109/TNS.2017.2676045.
Stinnett, Jacob, Sullivan, Clair J., and Xiong, Hao. Thu . "Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra". United States. doi:10.1109/TNS.2017.2676045. https://www.osti.gov/servlets/purl/1367512.
@article{osti_1367512,
title = {Uncertainty analysis of wavelet-based feature extraction for isotope identification on NaI gamma-ray spectra},
author = {Stinnett, Jacob and Sullivan, Clair J. and Xiong, Hao},
abstractNote = {Low-resolution isotope identifiers are widely deployed for nuclear security purposes, but these detectors currently demonstrate problems in making correct identifications in many typical usage scenarios. While there are many hardware alternatives and improvements that can be made, performance on existing low resolution isotope identifiers should be able to be improved by developing new identification algorithms. We have developed a wavelet-based peak extraction algorithm and an implementation of a Bayesian classifier for automated peak-based identification. The peak extraction algorithm has been extended to compute uncertainties in the peak area calculations. To build empirical joint probability distributions of the peak areas and uncertainties, a large set of spectra were simulated in MCNP6 and processed with the wavelet-based feature extraction algorithm. Kernel density estimation was then used to create a new component of the likelihood function in the Bayesian classifier. Furthermore, identification performance is demonstrated on a variety of real low-resolution spectra, including Category I quantities of special nuclear material.},
doi = {10.1109/TNS.2017.2676045},
journal = {IEEE Transactions on Nuclear Science},
number = 7,
volume = 64,
place = {United States},
year = {Thu Mar 02 00:00:00 EST 2017},
month = {Thu Mar 02 00:00:00 EST 2017}
}

Journal Article:
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