Title: Performance evaluation of cosmic ray muon trajectory estimation algorithms

Abstract

Muons, being elementary particles with minimal interaction with nuclear materials and abundant at sea level, have sparked interest in utilizing them for imaging various applications, such as mining [Borselli et al., Sci. Rep. 12, 22329 (2022)], volcano imaging [Nagamine et al., Nucl. Instrum. Meth. A, 356, 585(1995)], and underground tunnel detection [Guardincerri et al., Pure Appl. Geophys. 174, 2133 (2017)]. Recently, their use in nuclear nonproliferation and safeguard verification has gained attention, particularly in cargo screening for nuclear waste smuggling [Baesso et al., J. Instrum. 9, C10041 (2014)], source localization [L. J. Schultz et al., Nucl. Instrum. Meth. A 519, 687 (2004)], and locating nuclear fuel debris in reactors [Borozdin et al., Phys. Rev. Let. 109, 152501 (2012)]. However, the resolution of muon image reconstruction techniques is limited due to multiple Coulomb scattering (MCS) within the target object. To achieve robust muon tomography, it is crucial to develop efficient and flexible physics-based algorithms that can model the MCS process accurately and estimate the most probable trajectory of muons as they pass through the target object. To address this limitation, in this study, a novel algorithmic approach utilizing the Bayesian probability theory and Gaussian approximation of MCS is chosen. Different energymore » levels, materials, and target sizes were considered in the evaluations. The results demonstrate that the Generalized Muon Trajectory Estimation (GMTE) algorithm offers significant improvements over currently used algorithms. Across all test scenarios, the GMTE algorithm demonstrated ~50% and 38% increase in precision compared to Straight Line Path (SLP) and Point of Closest Approach (PoCA) algorithms, respectively. Furthermore, it exhibited 10%–35% and 10%–15% increases in muon flux utilization for high and medium Z materials, respectively, compared to the PoCA algorithm. In conclusion, the extensive simulations confirm the enhanced performance and efficiency of the GMTE algorithm, offering improved resolution and reduced measurement time for cosmic ray muon imaging compared to the current SLP and PoCA algorithms.« less

Authors:

Ughade, Reshma  ^[1];

• Search DOE PAGES for author "Ughade, Reshma"
• Search DOE PAGES for ORCID "0009-0002-1710-2348"

Bae, JungHyun  ^[2];

• Search DOE PAGES for author "Bae, JungHyun"
• Search DOE PAGES for ORCID "0000-0003-4785-9560"

Chatzidakis, Stylianos  ^[1]

• Search DOE PAGES for author "Chatzidakis, Stylianos"
• Search DOE PAGES for ORCID "0000-0003-1268-000X"

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+ Show Author Affiliations

1. Purdue Univ., West Lafayette, IN (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Publication Date:

Fri Dec 01 00:00:00 EST 2023

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

2234350

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

AIP Advances

Additional Journal Information:

Journal Volume: 13; Journal Issue: 12; Journal ID: ISSN 2158-3226

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

79 ASTRONOMY AND ASTROPHYSICS; cosmic rays; nuclear fuel; nuclear safety; nuclear waste; Coulomb scattering; computed tomography; tomography; Gaussian processes, Monte Carlo methods, Bayesian statistics