Scholarlink

<p>The particle identification (PID) of hadrons plays a crucial role in particle physics experiments, especially in flavor physics and jet tagging. The cluster counting method, which measures the number of primary ionizations in gaseous detectors, is a promising breakthrough in PID. However, developing an effective reconstruction algorithm for cluster counting remains challenging. To address this challenge, we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber. Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods. It improves the <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1670_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">\(K/\pi\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>K</mi> <mo stretchy="false">/</mo> <mi>π</mi> </mrow> </math></EquationSource> </InlineEquation> separation of PID by 10%, meeting the PID requirements of CEPC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cluster counting algorithm for the CEPC drift chamber using LSTM and DGCNN

  • 期刊论文

  • 发表日期：

    2025年5月7日

查看全文
  • Zhe-Fei Tian,
  • Guang Zhao,
  • Ling-Hui Wu,
  • Zhen-Yu Zhang,
  • Xiang Zhou,
  • Shui-Ting Xin,
  • Shuai-Yi Liu,
  • Gang Li,
  • Ming-Yi Dong,
  • Sheng-Sen Sun

摘要

The particle identification (PID) of hadrons plays a crucial role in particle physics experiments, especially in flavor physics and jet tagging. The cluster counting method, which measures the number of primary ionizations in gaseous detectors, is a promising breakthrough in PID. However, developing an effective reconstruction algorithm for cluster counting remains challenging. To address this challenge, we propose a cluster counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber. Experiments on Monte Carlo simulated samples demonstrate that our machine learning-based algorithm surpasses traditional methods. It improves the \(K/\pi\) K / π separation of PID by 10%, meeting the PID requirements of CEPC.