On-the-fly 3D muon path estimation algorithm for muon imaging applications

Muon tomography has gained attention for its potential use in critical national security applications, such as nuclear nonproliferation and safeguards verification, due to the unique properties of cosmic ray muons, including high energy and low attenuation even in dense materials. However, the resolution of muon image reconstruction is limited by multiple Coulomb scattering (MCS) within the target object. To improve and expand muon tomography, there is a need for efficient and flexible physics-based algorithms to model the MCS process accurately. In this study, we present a new algorithm based on Bayesian probability theory and a Gaussian approximation of MCS for estimating the most likely path of a cosmic ray muon as it traverses a uniform or nonuniform medium. Using GEANT4 simulations in a simple homogeneous 20 cm cube of low, medium, and high Z materials, we show that this algorithm can achieve reduced noise and better resolution for cosmic ray muons of various energies.