Material Identification in Nuclear Waste Drums using Muon Scattering Tomography and Multivariate Analysis

TL;DR

This paper demonstrates a muon scattering tomography system combined with multivariate analysis to accurately identify and locate materials like uranium, iron, and lead in nuclear waste drums, achieving high sensitivity and low false positive rates.

Contribution

It introduces a novel combination of feature discriminators and multivariate analysis for material identification in nuclear waste drums, improving detection accuracy and system performance.

Findings

Successfully identified uranium, iron, and lead objects on a few centimeter scale.

Achieved a uranium detection sensitivity of 0.90 with low false positive rate.

System performance was validated across various material configurations.

Abstract

The use of muon scattering tomography for the non-invasive characterisation of nuclear waste is well established. We report here on the application of a combination of feature discriminators and multivariate analysis techniques to locate and identify materials in nuclear waste drums. After successful training and optimisation of the algorithms they are then tested on a range of material configurations to assess the system's performance and limitations. The system is able to correctly identify uranium, iron and lead objects on a ~few \text{cm} scale. The system's sensitivity to small uranium objects is also established as $0.90^{+0.07}_{-0.12}$, with a false positive rate of $0.12^{+0.12}_{-0.07}$.