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Title: Radiation anomaly detection and classification with Bayes Model Selection

In this paper, we present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. Finally, the method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations.
Authors:
 [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Report Number(s):
PNNL-SA-133934
Journal ID: ISSN 0168-9002; PII: S0168900218308829
Grant/Contract Number:
AC05-76RL01830; DTRA10027-10507
Type:
Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 904; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE; Defense Threat Reduction Agency (DTRA) (United States)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; anomaly detection; gamma-ray spectroscopy; radiation monitoring; threat identification
OSTI Identifier:
1463334

Pfund, D. M.. Radiation anomaly detection and classification with Bayes Model Selection. United States: N. p., Web. doi:10.1016/J.NIMA.2018.07.047.
Pfund, D. M.. Radiation anomaly detection and classification with Bayes Model Selection. United States. doi:10.1016/J.NIMA.2018.07.047.
Pfund, D. M.. 2018. "Radiation anomaly detection and classification with Bayes Model Selection". United States. doi:10.1016/J.NIMA.2018.07.047.
@article{osti_1463334,
title = {Radiation anomaly detection and classification with Bayes Model Selection},
author = {Pfund, D. M.},
abstractNote = {In this paper, we present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. Finally, the method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations.},
doi = {10.1016/J.NIMA.2018.07.047},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = ,
volume = 904,
place = {United States},
year = {2018},
month = {7}
}