Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking

Graph Neural Networks for Charged Particle Tracking on FPGAs

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph -- nodes represent hits, while edges represent possible track segments -- and classifying the edges as true or fake track segments. However, their study in hardware- or…

量子图神经网络在带电粒子跟踪中的表征与升级

Characterization and upgrade of a quantum graph neural network for charged particle tracking

本文提出了一种量子图神经网络架构，用于高亮度下的带电粒子轨迹重建，通过结合经典和量子组件提升训练效率与性能。

Graph Neural Networks for 3D Multi-Object Tracking

3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work often uses a tracking-by-detection pipeline, where the feature of each object is extracted independently to compute an affinity matrix. Then, the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this pipeline is to learn discriminative features for different objects in order to reduce confusion during data association. To that end, we propose two innovative techniques: (1) instead of obtaining the features for each object independently, we propose a novel feature interaction …

ACORN（用于高能物理实验的几何深度学习径迹重建流水线）的不确定性量化与传播

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments

本文为 ACORN 粒子径迹重建流水线开发了基于蒙特卡洛 Dropout 的不确定性量化流程，在 TrackML 数据集上研究了不确定性传播及数据规模与几何物理性质的影响，证明该流水线具有高置信度且无 GNN 误校准问题。

基于图神经网络的在LHCb顶点探测器中的径迹寻找

Graph Neural Network-Based Track Finding in the LHCb Vertex Detector

本文提出一种基于图神经网络的径迹寻找流程，用于LHCb顶点探测器，通过批量执行和量化评估，在GPU上实现了优于经典算法的性能与成本平衡。

EggNet 径迹重建流水线的物理与计算性能

Physics and Computing Performance of the EggNet Tracking Pipeline

本文在完整 TrackML 数据集上评估了直接以探测器空间点为输入并迭代更新图结构的 EggNet 径迹重建流水线的物理与计算性能，并探索了降低计算内存和时间约束的技术。