Author(s)
Evan Groopman, Todd Williamson, Timothy Pope, Michael Bronikowski, Spencer M. Scott, Matthew Wellons
Abstract
We present important aspects of uranium particle age dating by Large-Geometry Secondary Ion Mass Spectrometry (LG-SIMS) that can introduce bias and increase model age uncertainties, especially for small, young, and/or low-enriched particles. This metrology is important for applications related to International Nuclear Safeguards. We explore influential factors related to model age estimation, including the effects of evolving surface chemistry on inter-element measurements of particles (e.g., Th and U), detector background, and aggregation methods using simulated and actual particle samples. We introduce a new model age estimator, called "mid68", that supplements 95% confidence intervals, providing a "best estimate" and uncertainty about the most likely age. The mid68 estimator can be calculated using the Feldman and Cousins method or Bayesian methods and provides a value with a symmetric uncertainty that can be used for calculations and approximate aggregation of processed model age values when the raw data and correction factors are not available. For particles yielding low 230Th counts amidst nonzero detector background, their underlying model age probability distributions are asymmetric, so the mid68 estimator provides additional robust information regarding the underlying model age likelihood. This study provides a comprehensive and timely examination of critical aspects of uranium particle age dating as more laboratories establish particle chronometry capabilities.
Keywords
SIMS, actinide particle metrology, nuclear safeguards, particle age dating, Poisson statistics
Uncertainty quantification, Statistical analysis, Metrology, Materials characterization and Chemistry
Citation
Groopman, E. , Williamson, T. , Pope, T. , Bronikowski, M. , Scott, S. and Wellons, M. (2025), Uranium Particle Age Dating, Aggregation, and Model Age Best Estimators, Analyst, [online], https://doi.org/10.1039/D5AN00249D, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959454 (Accessed June 14, 2025)
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