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

Abstract

Here, 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 Montemore » 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. Finally, the effect of energy loss due to ionization is investigated, and a linear energy loss relation that is easy to use is proposed.« less

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States). Department of Nuclear Engineering
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States). Department of Nuclear Engineering
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1430407
Alternate Identifier(s):
OSTI ID: 1430158; OSTI ID: 1454406
Grant/Contract Number:  
NE0008292; 8536; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Applied Physics
Additional Journal Information:
Journal Volume: 123; Journal Issue: 12; Journal ID: ISSN 0021-8979
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Chatzidakis, Stylianos, Liu, Zhengzhi, Hayward, Jason P., and Scaglione, John M. A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography. United States: N. p., 2018. Web. doi:10.1063/1.5024671.
Chatzidakis, Stylianos, Liu, Zhengzhi, Hayward, Jason P., & Scaglione, John M. A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography. United States. doi:10.1063/1.5024671.
Chatzidakis, Stylianos, Liu, Zhengzhi, Hayward, Jason P., and Scaglione, John M. Wed . "A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography". United States. doi:10.1063/1.5024671. https://www.osti.gov/servlets/purl/1430407.
@article{osti_1430407,
title = {A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography},
author = {Chatzidakis, Stylianos and Liu, Zhengzhi and Hayward, Jason P. and Scaglione, John M.},
abstractNote = {Here, 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. Finally, the effect of energy loss due to ionization is investigated, and a linear energy loss relation that is easy to use is proposed.},
doi = {10.1063/1.5024671},
journal = {Journal of Applied Physics},
number = 12,
volume = 123,
place = {United States},
year = {2018},
month = {3}
}

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