Bayesian inference of 1D activity profiles from segmented gamma scanning of a heterogeneous radioactive waste drum

TL;DR

This paper introduces a Bayesian method utilizing Hamiltonian Monte Carlo to accurately infer vertical activity profiles of radioactive waste within drums, accounting for measurement and source distribution uncertainties.

Contribution

It presents a novel Bayesian inference framework with HMC sampling for analyzing segmented gamma scanning data of heterogeneous radioactive waste drums.

Findings

Successfully applied to synthetic data demonstrating accuracy

Resolved activity distribution of 5 nuclides in real waste package

Effectively modeled uncertainties in measurements and source distribution

Abstract

We present a Bayesian approach to probabilistically infer vertical activity profiles within a radioactive waste drum from segmented gamma scanning (SGS) measurements. Our approach resorts to Markov chain Monte Carlo (MCMC) sampling using the state-of-the-art Hamiltonian Monte Carlo (HMC) technique and accounts for two important sources of uncertainty: the measurement uncertainty and the uncertainty in the source distribution within the drum. In addition, our efficiency model simulates the contributions of all considered segments to each count measurement. Our approach is first demonstrated with a synthetic example, after which it is used to resolve the vertical activity distribution of 5 nuclides in a real waste package.