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

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 2022-09, Vol.1039, p.166992, Article 166992
• Main Authors: Osthus, Dave, Mendoza, Paul, Lalor, Peter, Casleton, Emily, Archer, Dan, Ghawaly, James, Garishvili, Irakli, Rowe, Andrew J., Stewart, Ian R., Willis, Michael
• Format: Article
• Language: English
• Subjects: Bayesian; Constraints; Dynamic modeling; Inference; INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Machine learning; Uncertainty quantification
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2022.166992

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.