Scholarlink

<p>A Graph Neural Network (GNN) with an attention mechanism and two-stage aggregation for particle track reconstruction in the Time-Projection Chamber (TPC) of the Multi-Purpose Detector (MPD) of the Nuclotron-based Ion Collider fAcility (NICA) is presented. The model is trained and evaluated on a dataset of 1000 Au–Au collision events generated with MPDRoot. The model architecture includes node and edge encoders, an initial edge classifier, a graph attentional convolution layer, an edge embedding updater, and a final edge classifier, with the core blocks repeated for iterative refinement. A comparative analysis shows that the introduction of the attention mechanism and two-stage aggregation improves performance in edge classification, achieving 96.2% accuracy along with 92.6% in both purity and efficiency metrics. The track reconstruction efficiency exceeds 90% for track integrities below 80%, but rapidly drops for higher integrity thresholds.</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

Graph Neural Network with Attention and Two-Stage Aggregation for Particle Track Reconstruction in the TPC MPD of the NICA Accelerator Complex

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

查看全文
  • Yauheni Talochka,
  • Gennady Ososkov,
  • Nikolay Voytishin

摘要

A Graph Neural Network (GNN) with an attention mechanism and two-stage aggregation for particle track reconstruction in the Time-Projection Chamber (TPC) of the Multi-Purpose Detector (MPD) of the Nuclotron-based Ion Collider fAcility (NICA) is presented. The model is trained and evaluated on a dataset of 1000 Au–Au collision events generated with MPDRoot. The model architecture includes node and edge encoders, an initial edge classifier, a graph attentional convolution layer, an edge embedding updater, and a final edge classifier, with the core blocks repeated for iterative refinement. A comparative analysis shows that the introduction of the attention mechanism and two-stage aggregation improves performance in edge classification, achieving 96.2% accuracy along with 92.6% in both purity and efficiency metrics. The track reconstruction efficiency exceeds 90% for track integrities below 80%, but rapidly drops for higher integrity thresholds.