Title: Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction Show Chinese title

Title: 基于局部敏感哈希的高效点变换器用于带电粒子重建

Abstract: Charged particle track reconstruction is a foundational task in collider experiments and the main computational bottleneck in particle reconstruction. Graph neural networks (GNNs) have shown strong performance for this problem, but costly graph construction, irregular computations, and random memory access patterns substantially limit their throughput. The recently proposed Hashing-based Efficient Point Transformer (HEPT) offers a theoretically guaranteed near-linear complexity for large point cloud processing via locality-sensitive hashing (LSH) in attention computations; however, its evaluations have largely focused on embedding quality, and the object condensation pipeline on which HEPT relies requires a post-hoc clustering step (e.g., DBScan) that can dominate runtime. In this work, we make two contributions. First, we present a unified, fair evaluation of physics tracking performance for HEPT and a representative GNN-based pipeline under the same dataset and metrics. Second, we introduce HEPTv2 by extending HEPT with a lightweight decoder that eliminates the clustering stage and directly predicts track assignments. This modification preserves HEPT's regular, hardware-friendly computations while enabling ultra-fast end-to-end inference. On the TrackML dataset, optimized HEPTv2 achieves approximately 28 ms per event on an A100 while maintaining competitive tracking efficiency. These results position HEPTv2 as a practical, scalable alternative to GNN-based pipelines for fast tracking. Show Chinese abstract

Abstract: 带电粒子轨迹重建是对撞机实验中的基础任务，也是粒子重建中的主要计算瓶颈。图神经网络（GNNs）在该问题上表现出色，但昂贵的图构建、不规则的计算和随机的内存访问模式显著限制了它们的吞吐量。最近提出的基于哈希的高效点变换器（HEPT）通过注意力计算中的局部敏感哈希（LSH）实现了大规模点云处理的理论保证近线性复杂度；然而，其评估主要集中在嵌入质量上，而HEPT所依赖的对象凝聚流程需要一个事后聚类步骤（例如，DBScan），这可能会主导运行时间。在本工作中，我们做出了两项贡献。首先，我们在相同的数据集和指标下，对HEPT和一种代表性的基于GNN的流程进行了统一且公平的物理轨迹性能评估。其次，我们通过扩展HEPT引入了HEPTv2，其轻量解码器消除了聚类阶段，直接预测轨迹分配。这种修改保留了HEPT的规则、硬件友好的计算，同时实现了超快速的端到端推理。在TrackML数据集上，优化后的HEPTv2在A100上每个事件的处理时间约为28毫秒，同时保持了具有竞争力的轨迹重建效率。这些结果使HEPTv2成为基于GNN的流程在快速轨迹重建中的实用且可扩展的替代方案。