$\mu$TRec: A Muon Trajectory Reconstruction Algorithm for Enhanced Scattering Tomography
Reshma Ughade, Stylianos Chatzidakis (School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA)

TL;DR
The paper introduces $TRec, a Bayesian muon trajectory reconstruction algorithm that significantly improves imaging accuracy and resolution in scattering tomography of dense structures like nuclear fuel casks, outperforming traditional methods.
Contribution
It presents a novel Bayesian approach for muon path modeling that enhances imaging precision and enables high-resolution detection of structural components and missing assemblies.
Findings
$TRec achieves 122% SNR improvement over PoCA.
It enables detection of missing fuel assemblies at lower muon flux.
Supports high-resolution imaging with voxel sizes as small as 1 cm.
Abstract
Cosmic ray muons enable non-invasive imaging of dense structures through multipleCoulomb scattering (MCS), with scattering angles dependent on atomic number (Z). Traditional algorithms like Point of Closest Approach (PoCA) assume single scattering, limiting accuracy. This work presents the TRec algorithm, which models muon paths using a Bayesian approach with Gaussian approximations, accounting for MCS and energy loss. TRec is applied to simulate muon imaging of dry storage casks (DSCs) used for spent nuclear fuel, with four loading configurations: fully loaded, one row missing, one assembly missing, and half assembly missing. The results demonstrate improved accuracy and resolution in identifying missing assemblies compared to conventional methods. It is observed that the TRec algorithm exhibits markedly superior performance over the classical PoCA method achieving…
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