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<p><b>Purpose</b> Cosmic ray muons, characterized by their high energy and penetrative capabilities, provide significant advantages for non-destructive imaging applications, including security inspection, geological exploration, and archaeology. As the muon tomography continues to advance, there is growing demand for precise and efficient muon imaging algorithms. This study aims to enhance muon track reconstruction accuracy, improve the quality of muon scattering imaging, and increase track utilization quality. <b>Methods</b> This paper proposes a neural network-based method utilizing Multi-Wire Drift Chambers (MWDCs), to improve muon track reconstruction performance. Additionally, to address the limitations of the conventional Point-of-Closest Approach (PoCA) algorithm in imaging accuracy and track utilization efficiency, an improved PoCA-based imaging method is proposed and its imaging performance is evaluated. <b>Results</b> The proposed neural network-based track reconstruction method achieved a spatial resolution 351 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mu m\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>μ</mi> <mi>m</mi> </mrow> </math></EquationSource> </InlineEquation>. Furthermore, the improved PoCA algorithm significantly improved imaging resolution and reconstruction performance, while enhancing muon track utilization efficiency. <b>Conslusions</b> The MWDC-based neural network track reconstruction method improves muon track reconstruction performance, while the improved PoCA algorithm enhances imaging quality and track utilization efficiency. The combination of these methods provides an effective solution for muon scattering tomography</p>

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An improved PoCA-based algorithm with neural network track reconstruction for muon scattering tomography

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Jielei Zhang,
  • Xiangman Liu,
  • Zhiyu Sun,
  • Fang Fang,
  • Qi An,
  • Fenhua Lu,
  • Shuwen Tang,
  • Herun Yang,
  • Peng Ma,
  • Yi Qian,
  • Jie Kong,
  • Shitao Wang,
  • Xuan Jiang,
  • Yazhou Sun,
  • Duo Yan,
  • Xueheng Zhang,
  • Yongjie Zhang,
  • Yuhong Yu,
  • Jun Jiang

摘要

Purpose Cosmic ray muons, characterized by their high energy and penetrative capabilities, provide significant advantages for non-destructive imaging applications, including security inspection, geological exploration, and archaeology. As the muon tomography continues to advance, there is growing demand for precise and efficient muon imaging algorithms. This study aims to enhance muon track reconstruction accuracy, improve the quality of muon scattering imaging, and increase track utilization quality. Methods This paper proposes a neural network-based method utilizing Multi-Wire Drift Chambers (MWDCs), to improve muon track reconstruction performance. Additionally, to address the limitations of the conventional Point-of-Closest Approach (PoCA) algorithm in imaging accuracy and track utilization efficiency, an improved PoCA-based imaging method is proposed and its imaging performance is evaluated. Results The proposed neural network-based track reconstruction method achieved a spatial resolution 351 \(\mu m\) μ m . Furthermore, the improved PoCA algorithm significantly improved imaging resolution and reconstruction performance, while enhancing muon track utilization efficiency. Conslusions The MWDC-based neural network track reconstruction method improves muon track reconstruction performance, while the improved PoCA algorithm enhances imaging quality and track utilization efficiency. The combination of these methods provides an effective solution for muon scattering tomography