Reconstructing North Korea's Plutonium Production History with Bayesian Inference-Based Reprocessing Waste Analysis

TL;DR

This paper demonstrates how Bayesian inference applied to reprocessing waste isotopic analysis can reconstruct North Korea's plutonium production history, aiding verification in disarmament efforts.

Contribution

It introduces a Bayesian inference framework for analyzing nuclear waste to determine reactor operation history and plutonium production, enhancing nuclear archaeology methods.

Findings

Correct prior assumptions can be confirmed.

Incorrect priors or false declarations can be detected.

Model comparison distinguishes different reactor operation scenarios.

Abstract

Although North Korea's nuclear program has been the subject of extensive scrutiny, estimates of its fissile material stockpiles remain fraught with uncertainty. In potential future disarmament agreements, inspectors may need to use nuclear archaeology methods to verify or gain confidence in a North Korean fissile material declaration. This study explores the potential utility of a Bayesian inference-based analysis of the isotopic composition of reprocessing waste to reconstruct the operating history of the 5 MWe reactor and estimate its plutonium production history. We simulate several scenarios that reflect different assumptions and varying levels of prior knowledge about the reactor. The results show that correct prior assumptions can be confirmed and incorrect prior information (or a false declaration) can be detected. Model comparison techniques can distinguish between scenarios with different numbers of core discharges, a capability that could provide important insights into the early stages of operation of the 5 MWe reactor. Using these techniques, a weighted plutonium estimate can be calculated, even in cases where the number of core discharges is not known with certainty.