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 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.
Highlights
The training Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) for these classifiers show that the lead and uranium cases are distinguished from iron, whereas the lead/uranium classifier does not perform as well, due to the similarity of the materials’ Z values
We have demonstrated that machine learning techniques are a powerful tool for enhancing the information about a waste drum’s contents that can be obtained in a muon scattering tomography experiment
MVA classifiers trained on variables obtained from the distribution of binned clustering algorithm metric values are effective at discriminating materials in waste drums
Summary
Cosmic rays interact with the Earth’s atmosphere to produce showers of particles, some of which subsequently decay to muons, resulting in a muon flux at sea level of around 1 cm−2 min−1 [7] These cosmic ray muons are highly penetrating due to their large mass and lack of strong interactions. The simplest is the Point of Closest Approach (PoCA) algorithm [10], which models a muon’s multiple scatterings as a single scattering at a single point (‘scattering vertex’), found by extrapolating the incoming and outgoing tracks into the volume and finding the point which minimises the distance to each This assumption allows for fast computation at the expense of image quality. A more advanced MST algorithm has been used in this study (see section 2.1) which builds on PoCA by exploiting the spatial density of scattering vertices; a high density of scattering vertices corresponds to the presence of high-Z material as large-angle muon scatterings occur more often in such materials
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