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Recent discrepancies in the muon’s anomalous magnetic moment, \(a_{\mu }\) , between the Standard Model prediction and experimental measurements suggest potential new physics. The J-PARC muon g-2/EDM experiment aims to measure \(a_{\mu }\) with 460 ppb precision using reaccelerated ultraslow muons in a magnetic storage ring. The reconstruction of positron tracks from muon decays is done using the Hough Transformation algorithm, but achieving the required 10x speedup is crucial. To address this, we are exploring optimization strategies using GPUs and Graph Neural Networks (GNNs).

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Track Finding Using GNNs and GPUs for the J-PARC muon g-2/EDM Experiment

  • 会议论文

  • 发表日期：

    2025年

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  • H. Chetri,
  • S. Sandilya,
  • D. Samuel,
  • T. Mibe,
  • T. Yamanaka,
  • T. Suehara

摘要

Recent discrepancies in the muon’s anomalous magnetic moment, \(a_{\mu }\) , between the Standard Model prediction and experimental measurements suggest potential new physics. The J-PARC muon g-2/EDM experiment aims to measure \(a_{\mu }\) with 460 ppb precision using reaccelerated ultraslow muons in a magnetic storage ring. The reconstruction of positron tracks from muon decays is done using the Hough Transformation algorithm, but achieving the required 10x speedup is crucial. To address this, we are exploring optimization strategies using GPUs and Graph Neural Networks (GNNs).