Statistical Methods Applied to Gamma-ray Spectroscopy Algorithms in Nuclear Security Missions

Journal Article · Mon Oct 01 04:00:00 EDT 2012 · Applied Radiation and Isotopes, 70(10):2428-2439

DOI:https://doi.org/10.1016/j.apradiso.2012.06.016· OSTI ID:1056149

Fagan, Deborah K; Robinson, Sean M; Runkle, Robert C

In a wide range of nuclear security missions, gamma-ray spectroscopy is a critical research and development priority. One particularly relevant challenge is the interdiction of special nuclear material for which gamma-ray spectroscopy supports the goals of detecting and identifying gamma-ray sources. This manuscript examines the existing set of spectroscopy methods, attempts to categorize them by the statistical methods on which they rely, and identifies methods that have yet to be considered. Our examination shows that current methods effectively estimate the effect of counting uncertainty but in many cases do not address larger sources of decision uncertainty—ones that are significantly more complex. We thus explore the premise that significantly improving algorithm performance requires greater coupling between the problem physics that drives data acquisition and statistical methods that analyze such data. Untapped statistical methods, such as Bayes Modeling Averaging and hierarchical and empirical Bayes methods have the potential to reduce decision uncertainty by more rigorously and comprehensively incorporating all sources of uncertainty. We expect that application of such methods will demonstrate progress in meeting the needs of nuclear security missions by improving on the existing numerical infrastructure for which these analyses have not been conducted.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1056149
• Report Number(s): PNNL-SA-83438; NN2001000
• Journal Information: Applied Radiation and Isotopes, 70(10):2428-2439, Journal Name: Applied Radiation and Isotopes, 70(10):2428-2439
• Country of Publication: United States
• Language: English

Related Subjects

detection algorithm.
gamma-ray spectroscopy
special nuclear material identification