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Statistical methods applied to gamma-ray spectroscopy algorithms in nuclear security missions.
Deborah K Fagan1, Sean M Robinson, Robert C Runkle
1Pacific Northwest National Laboratory, Richland, WA 99352, USA. dfagan@pnnl.gov
Advanced statistical methods can improve gamma-ray spectroscopy for nuclear security. New approaches like Bayes modeling averaging can reduce decision uncertainty in detecting special nuclear materials.
Area of Science:
- Nuclear physics
- Statistical analysis
- Nuclear security
Background:
- Gamma-ray spectroscopy is vital for nuclear security missions, particularly detecting special nuclear materials.
- Current methods often focus on counting uncertainty, neglecting more complex decision uncertainties.
Purpose of the Study:
- To categorize existing statistical methods in gamma-ray spectroscopy.
- To identify and propose novel statistical approaches for improved nuclear material detection.
Main Methods:
- Categorization of current gamma-ray spectroscopy methods based on statistical underpinnings.
- Exploration of untapped statistical techniques, including Bayes Modeling Averaging and hierarchical/empirical Bayes methods.
Main Results:
- Existing methods inadequately address complex decision uncertainties in gamma-ray source identification.
- Novel statistical methods offer a rigorous way to incorporate all uncertainty sources, potentially reducing decision uncertainty.
Conclusions:
- Integrating problem physics with advanced statistical methods is key to enhancing algorithm performance.
- Untapped statistical methods can significantly improve nuclear security by reducing decision uncertainty and enhancing detection capabilities.

