The Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Defense Nuclear Nonproliferation Research and Development (DNN R&D) program has invested in researching the use of data science methods to improve the detection of diverted material, facility misuse, and other undeclared activities of concern. Nuclear safeguards is a data-rich field that is ideal for the application of modern data analytic techniques. Application of data analytics and machine learning to test data sets and controlled safeguard situations will yield insights that will mature these techniques for future deployment. This multi-lab effort uses actual data obtained from an operating nuclear facility at the Oak Ridge National Laboratory (ORNL) for functional evaluations. The projects are investigating the use of common and new data science methods, with the goal of developing safeguards-specific algorithms and methodologies that may require the utilization of new datasets, not currently used by domestic or international safeguards programs. This work will report on the completion of initial efforts, focused on developing safeguards specific data science methods. Areas of research include examining facility behavioral patterns, data surety, anomaly detection, rapid handling, and analysis of large datasets.
Year
2020
Abstract