A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography

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 non-intrusive 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, e.g., point of closest approach (PoCA) or straight-line path (SLP), 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% improvement compared to SLP and 15% improvement when compared to PoCA. Further, a 30% 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.

Principle study of image reconstruction algorithms in muon tomography

“…MLP represents the statistical most probable trajectory of muons that entering and exiting at the same points and angles. For charged particles that energy loss in the medium is not negligible, the elements of covariance matrix become integral expressions to include this factor [19]. Since the prior of scattering densities in the object is not known, homogeneous medium is often assumed that will smooth the actual trajectory of muon.…”

Advances in Cosmic Ray Muon Computed Tomography and Fieldable Spectroscopy

“…The muons then pass through the object before hitting and interacting with the second plane of detectors where their final positions are also recorded. This information is then processed using monitoring, classification and imaging algorithms [22][23][24][25][26][27]. Fig.…”

“…2. GEANT4 model construction (left) and 2D reconstruction using two different imaging algorithms (right) [22][23][24][25][26][27].…”

Design and Optimization of Motion Training System Assisted by Human Posture Estimation Algorithm

“…As for the design and optimization of sports training system, a large number of researchers and research institutions have studied and analyzed it. e conventional methods mainly include image method, graph analysis method, portable sensor algorithm, and so on [15][16][17][18][19]. For the human target detection level, a large number of algorithms are also emerging and improve the effect of sports training system at different levels.…”