Tracking the location of a road-constrained radioactive source with a network of detectors

Dave Osthus, Paul Mendoza, Peter Lalor, Emily Casleton, Dan Archer, James Ghawaly, Irakli Garishvili, Andrew J. Rowe, Ian R. Stewart, Michael Willis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Data collected from a network of detectors and analyzed together has the opportunity to provide a more complete picture than when the data from each individual detector are analyzed independently. However, even with a dense array of detectors, the network data will not provide a complete picture and constraints will need to be added to the model in order to maximize the usefulness of the conclusions that can be drawn. In this work, we demonstrate this concept by considering the task of tracking a moving radioactive source of special nuclear material in a structured environment with data from a network of radiation detectors. Our approach uses a Bayesian model and analysis that naturally provides uncertainty in the estimate of the source's dynamic location. We find that adding domain aware constraints to a Bayesian model (e.g., the location of the road) can improve both location inference and do so with diminished uncertainty even though the fit to gamma count data is largely unchanged.

Funding

This work was supported by the U.S. Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001). Approved for public release LA-UR-21-31227. This work was supported by the U.S. Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001 ). Approved for public release LA-UR-21-31227.

Keywords

  • Bayesian
  • Constraints
  • Dynamic modeling
  • Inference
  • Machine learning
  • Uncertainty quantification

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