Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions

• Published in: Applied radiation and isotopes Vol. 70; no. 10; pp. 2428 - 2439
• Main Authors: Fagan, Deborah K., Robinson, Sean M., Runkle, Robert C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-10-2012
• Subjects: Detection algorithms; Gamma-ray spectroscopy; Special nuclear material identification; Gamma-ray spectroscopy; Special nuclear material identification; Detection algorithms
• ISSN: 0969-8043; 1872-9800
• DOI: 10.1016/j.apradiso.2012.06.016

Gamma-ray spectroscopy is a critical research and development priority to a range of nuclear security missions, specifically the interdiction of special nuclear material involving the detection and identification of gamma-ray sources. We categorize existing methods by the statistical methods on which they rely and identify methods that have yet to be considered. Current methods estimate the effect of counting uncertainty but in many cases do not address larger sources of decision uncertainty, which may be significantly more complex. Thus, significantly improving algorithm performance may require 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, could reduce decision uncertainty by rigorously and comprehensively incorporating all sources of uncertainty. Application of such methods should further meet the needs of nuclear security missions by improving upon the existing numerical infrastructure for which these analyses have not been conducted.
► We review and evaluate gamma-ray spectroscopy for nuclear security. ► We categorize existing algorithms by class of statistical approach and evaluate literature gaps. ► Current approaches may not fully address decision uncertainty, limiting performance. ► Methods not yet explored by the literature may provide further for improvement.