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Bayesian Analysis of Energy and Count Rate Data for Detection of Low Count Rate Radioactive Sources

[+] Author Affiliations
John Klumpp

Colorado State University, Fort Collins, CO

Paper No. ICEM2013-96357, pp. V002T03A050; 5 pages
doi:10.1115/ICEM2013-96357
From:
  • ASME 2013 15th International Conference on Environmental Remediation and Radioactive Waste Management
  • Volume 2: Facility Decontamination and Decommissioning; Environmental Remediation; Environmental Management/Public Involvement/Crosscutting Issues/Global Partnering
  • Brussels, Belgium, September 8–12, 2013
  • Conference Sponsors: Nuclear Engineering Division, Environmental Engineering Division
  • ISBN: 978-0-7918-5602-4
  • Copyright © 2013 by ASME

abstract

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a “machine learning” approach, in which the algorithm updates and improves its characterization of background over time. The system would have a “learning mode,” in which it measures and analyzes background count rates, and a “detection mode,” in which it compares measurements from an unknown source against its unique background distribution.

By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a non-parametric sampling distribution.

Copyright © 2013 by ASME

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