2021/01/06 by Eric Laloy, Bart Rogiers, Laloy, Eric +5
Chemistry · Engineering · Health Professions · Mathematics · Physics and Astronomy · #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Data Analysis #Drum #Engineering #Environmental science #FOS: Physical sciences #Gibbs sampling #Hybrid Monte Carlo #Inference #Instrumentation and Detectors (physics.ins-det) #Markov chain Monte Carlo #Mathematics #Mechanical engineering #Monte Carlo method #Nuclear physics #Nuclear reactor physics and engineering #Nuclide #Physics #Radioactive element chemistry and processing #Radioactive waste #Radioactivity and Radon Measurements #Statistics #Statistics and Probability (physics.data-an) #Waste management #physics.data-an #physics.ins-det
paper · pdf · doi:10.48550/arxiv.2101.02112
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/01/06 · arxiv created 2021/03/29 · arxiv updated 2021/03/30 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/04
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.