A Generalized Muon Trajectory Estimation Algorithm with Energy Loss for Application to Muon Tomography

TL;DR

This paper introduces a generalized Bayesian muon trajectory estimation algorithm that accounts for energy loss and multiple Coulomb scattering, significantly improving accuracy and flux utilization in muon tomography applications.

Contribution

The work develops a physics-based, Bayesian algorithm for muon path estimation that outperforms existing methods by incorporating energy loss and nonuniform media modeling.

Findings

Achieves less than 1.5 mm RMS prediction for 0.5 GeV muons

Improves prediction accuracy by 50% over SLP and 15% over PoCA

Increases useful muon flux by 30% compared to PoCA

Abstract

This work presents a generalized muon trajectory estimation (GMTE) algorithm to estimate the path of a muon in either uniform or nonuniform media. The use of cosmic ray muons in nuclear nonproliferation and safeguards verification applications has recently gained attention due to the nonintrusive and passive nature of the inspection, penetrating capabilities, as well as recent advances in detectors that measure position and direction of the individual muons before and after traversing the imaged object. However, muon image reconstruction techniques are limited in resolution due to low muon flux and the effects of multiple Coulomb scattering (MCS). Current reconstruction algorithms rely on overly simple assumptions for muon path estimation through the imaged object. For robust muon tomography, efficient and flexible physics based algorithms are needed to model the MCS process and accurately estimate the most probable trajectory of a muon as it traverses an object. In the present work, the use of a Bayesian framework and a Gaussian approximation of MCS are explored for estimation of the most likely path of a cosmic ray muon traversing uniform or nonuniform media and undergoing MCS. The algorithm's precision is compared to Monte Carlo simulated muon trajectories. It was found that the algorithm is expected to be able to predict muon tracks to less than 1.5 mm RMS for 0.5 GeV muons and 0.25 mm RMS for 3 GeV muons, a 50 percent improvement compared to SLP and 15 percent improvement when compared to PoCA. Further, a 30 percent increase in useful muon flux was observed relative to PoCA. Muon track prediction improved for higher muon energies or smaller penetration depth where energy loss is not significant. The effect of energy loss due to ionization is investigated, and a linear energy loss relation that is easy to use is proposed.