Benjamin Jung

RWTH Aachen University

Zoom link: https://mit.zoom.us/j/91794151221

Abstract:

Like the non-proliferation regime today, future nuclear disarmament efforts will likely have to deal with stockpiles of weapons-usable fissile material and the verification of declarations about these stockpiles. To support such verification tasks, nuclear archaeology offers scientific methods to reconstruct the operational history of nuclear facilities. The Bayesian Reprocessing waste Analysis Method (BRAM) is a promising new approach that attempts to reconstruct information about the operating history of a nuclear reactor from reprocessing waste samples using a Bayesian inference framework. Given some prior information, computational reactor models and measurements of isotopic ratios in the reprocessing waste, the Bayesian framework reconstructs a posterior probability distribution for parameters of interest such as average burnup, initial enrichment and cooling time of the fuel.

Our simulation-based tests demonstrate the potential of this method to reconstruct the parameters even when the reactor of origin is unknown or when waste from different core charges has been mixed. We demonstrate possible applications of the framework in a simulated case study of the North Korean 5 MWe reactor. Although further testing with real measurement data is required to understand the challenges and associated uncertainties, our results demonstrate the potential insights that this framework can provide and how it can contribute to the verification of fissile material declarations.

Bio:

Benjamin Jung is a doctoral student in physics at RWTH Aachen University and works as a research associate at the Technical University of Darmstadt. His research interests are arms control verification, nuclear archaeology, and statistical inference. Benjamin is currently working on developing new nuclear archaeology methods for reconstructing reactor operating histories from samples of nuclear reprocessing waste.