Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis

Michael Weekes; Ahmad Alrheli; Dominic Barker; Daniel Kikola; Anna K Kopp; Mohammed Mhaidra; Patrick Stowell; Lee Thompson; Jaap J Velthuis

doi:10.1088/1748-0221/16/05/P05007

Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis

Michael Weekes
, Ahmad Alrheli
, Dominic Barker
, Daniel Kikola
, Anna K Kopp
, Mohammed Mhaidra
, Patrick Stowell
, Lee Thompson
, Jaap J Velthuis

School of Physics

Research output: Contribution to journal › Article (Academic Journal) › peer-review

9 Citations (Scopus)

118 Downloads (Pure)

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.

Original language	English
Article number	P05007
Number of pages	27
Journal	Journal of Instrumentation
Volume	16
Issue number	5
DOIs	https://doi.org/10.1088/1748-0221/16/05/P05007
Publication status	Published - 6 May 2021

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© 2021 The Author(s).

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10.1088/1748-0221/16/05/P05007

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title = "Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis",

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{\textquoteright}s performance and limitations. The system is able to correctly identify uranium, iron and lead objects on a few cm scale. The system{\textquoteright}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.",

author = "Michael Weekes and Ahmad Alrheli and Dominic Barker and Daniel Kikola and Kopp, \{Anna K\} and Mohammed Mhaidra and Patrick Stowell and Lee Thompson and Velthuis, \{Jaap J\}",

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T1 - Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis

AU - Weekes, Michael

AU - Alrheli, Ahmad

AU - Barker, Dominic

AU - Kikola, Daniel

AU - Kopp, Anna K

AU - Mhaidra, Mohammed

AU - Stowell, Patrick

AU - Thompson, Lee

AU - Velthuis, Jaap J

PY - 2021/5/6

Y1 - 2021/5/6

N2 - 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.

AB - 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.

U2 - 10.1088/1748-0221/16/05/P05007

DO - 10.1088/1748-0221/16/05/P05007

M3 - Article (Academic Journal)

SN - 1748-0221

VL - 16

JO - Journal of Instrumentation

JF - Journal of Instrumentation

IS - 5

M1 - P05007

ER -

Material identification in nuclear waste drums using muon scattering tomography and multivariate analysis

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