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Nuclear material accountancy using momentum-informed muon scattering tomography...

by Junghyun Bae, Rosemary A Montgomery, Stylianos Chatzidakis
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Annals of Nuclear Energy
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Screening cargo and vehicles to prevent proliferation of unauthorized nuclear and radioactive materials is essential to prevent threats to domestic and international security. Cosmic ray muons attracted attention as a potential next-generation radiographic technique to discover illicit transportation of nuclear and radioactive materials at borders or secure facilities. Unique features of cosmic muons—a high energy and deep penetration depth—enable them to be utilized for radiation probes, especially for large and dense materials. In muon scattering tomography, physical characteristics of target materials are estimated by measuring both incoming and outgoing muon trajectories using muon detectors. To reconstruct 3D tomographic images of target objects, a Point of Closest Approach (PoCA) algorithm is typically used to locate a scattering position, and the scattering angle values are used to map material density. However, high-resolution material mapping is often limited due to the absence of muon momentum information because the expected muon scattering angle depends on two variables: material atomic number and muon momentum. Despite the importance of measuring muon momentum, it is still challenging without deploying large and expensive muon spectrometers. Therefore, a mean muon momentum, 3 to 4 GeV/c, is often used to represent the entire cosmic ray muon spectrum. In this paper, we present a new momentum-informed muon scattering tomography (MST) approach for nuclear material accountancy based on a recently devised fieldable muon spectrometer. This approach uses a new imaging algorithm that improves the current PoCA algorithm by encoding momentum into the scattering angle. In our new algorithm, all muon measurements have a categorized momentum level with a scattering angle. We demonstrate the functionality and performance of momentum-informed MST using computational simulations. The results show that the image resolution is significantly improved, and this approach allows for visually differentiating nuclear materials from non-nuclear materials.