Speaker
Michael Weekes
Description
In addition to imaging an object, muon scattering tomography (MST) benefits from providing additional information, i.e. the Z of the scattering material, when interrogating the object. MST’s potential therefore to image the material inside nuclear waste drums is well-established. Here the technique is extended, using machine learning methods, to provide quantifiable metrics for in-drum material identification. The methods thus developed and the results obtained for a series of nominal material types and shapes in a simulated drum are presented. Results using the MST technique to determine whether or not materials have been diverted or replaced in the drum will also be presented.
Primary author
Co-authors
Lee Thompson
(University of Sheffield)
Jaap Velthuis
Ahmad F Alrheli
Chiara De Sio
(University of Bristol)
Dominic J Barker
Daniel Kikola
Anna Kopp
(Albert-Ludwigs-Universitaet Freiburg (DE))
Mohammed Mhaidra
Patrick Stowell
(Durham University (UK))
