Graph neural networks (GNNs) have gained traction in high-energy physics (HEP) for their potential to improve accuracy and scalability. However, their resource-intensive nature and complex operations have motivated the development of symmetry-equivariant architectures. In this work, we introduce EuclidNet, a novel symmetry-equivariant GNN for charged particle tracking. EuclidNet leverages the graph representation of collision events and enforces rotational symmetry with respect to the detector's beamline axis, leading to a more efficient model. We benchmark EuclidNet against the state-of-the-art Interaction Network on the TrackML dataset, which simulates high-pileup conditions expected at the High-Luminosity Large Hadron Collider (HL-LHC). Our results show that EuclidNet achieves near-state-of-the-art performance at small model scales (<1000 parameters), outperforming the non-equivariant benchmarks. This study paves the way for future investigations into more resource-efficient GNN models for particle tracking in HEP experiments.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
University of California San Diego（加州大学圣地亚哥分校）；Princeton University（普林斯顿大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Massachusetts Institute of Technology（麻省理工学院）；Institute of Physics Belgrade（贝尔格莱德物理研究所）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织（CERN））；California Institute of Technology（加州理工学院）；Purdue University（普渡大学）；HawkEye360；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Graph Neural Networks for Charged Particle Tracking on FPGAs
University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加州大学圣地亚哥分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Princeton University（普林斯顿大学）；University of Washington（华盛顿大学）
Geometric GNNs for Charged Particle Tracking at GlueX
Thomas Jefferson National Accelerator Facility（托马斯·杰斐逊国家加速器设施）；Department of Computer Science, University of Houston（休斯顿大学计算机科学系）；Department of Physics, William & Mary（威廉与玛丽学院物理系）；Computer Science Department, Old Dominion University（老 Dominion 大学计算机科学系）
Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction
Georgia Institute of Technology（佐治亚理工学院）；Purdue University（普渡大学）；University of Washington（华盛顿大学）；Princeton University（普林斯顿大学）；University of California San Diego（加州大学圣地亚哥分校）
HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Department of Physics and Astronomy, Purdue University（普渡大学物理与天文学系）；Department of Physics, University of California San Diego（加州大学圣地亚哥分校物理系）；Department of Physics, University of Washington（华盛顿大学物理系）
High Pileup Particle Tracking with Object Condensation
Princeton University（普林斯顿大学）；Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP)（高能物理软件研究与创新研究所（IRIS-HEP））；Delhi Technological University（德里理工大学）；University of Chicago（芝加哥大学）；Georgia Tech（佐治亚理工学院）
Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders
Istituto Nazionale di Fisica Nucleare, Sezione di Firenze（意大利佛罗伦萨核物理研究所）；ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data & Quantum Computing（高性能计算、大数据与量子计算国家研究中心）
Impact of Cold Noise on the tracking performance of ATLAS ITk short strip barrel modules using a charged particle beam
Santa Cruz Institute for Particle Physics, University of California Santa Cruz（加州大学圣克鲁兹分校圣克鲁兹粒子物理研究所）；Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器中心）；Lunds universitet（隆德大学）；University of Birmingham（伯明翰大学）；Charles University（查理大学）；Rutherford Appleton Laboratory（卢瑟福阿普尔顿实验室）；University of British Columbia（不列颠哥伦比亚大学）；TRIUMF（特里姆夫实验室）
Charged particle tracking without magnetic field: optimal measurement of track momentum by a Bayesian analysis of the multiple measurements of deflections due to multiple scattering
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Cornell University（康奈尔大学）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）；Northeastern University（东北大学）；Purdue University（普渡大学）；University of Chicago（芝加哥大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
First tracking performance results from the ATLAS Fast TracKer
大型强子对撞机的粒子物理学家通过将高能质子束碰撞在一起并观察碰撞的衰变产物来研究亚原子长度尺度下物质的性质。ATLAS是重建相互作用的两个通用探测器之一，作为广泛物理目标的一部分，测量希格斯玻色子的产生并寻找奇异的新现象，包括超对称性、时空的额外维度和暗物质。使用基于硬件和软件的触发器选择有趣的碰撞事件是一个主要挑战，随着未来数据中亮度的增加，这将变得更加困难。ATLAS Fast TracKer（FTK）是一种定制的电子系统，可对带电粒子进行基于硬件的快速跟踪，用于触发决策。2018年，使用质子-质子碰撞安装并调试了覆盖ATLAS探测器部分的两个FTK“Slices”，为运行3中的物理数据采集做准备。给出了FTK跟踪算法的寻轨和航迹拟合策略以及FTK切片的跟踪性能。讨论了在未来数据中应对变化的波束点和其他条件的策略。还提出了一种用于触发长寿命粒子的位移轨道的策略。
Impact of front-end parameters of the ARCADIA MD3 on charged particle detection
University of Padova（帕多瓦大学）；Centro di Ateneo di Studi e Attività Spaziali CISAS "G. Colombo", University of Padova（帕多瓦大学 CISAS "G. Colombo" 大学中心空间研究与活动中心）；University of Cassino and Southern Lazio（卡西诺及南拉齐奥大学）；CERN（欧洲核子研究组织）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；University of Florida（佛罗里达大学）；California Institute of Technology（加州理工学院）；A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute)（A.I.阿里汉尼扬国家科学实验室（埃里温物理研究所））；INFN TIFPA, and Department of Industrial Engineering, University of Trento（特伦托大学工业工程系与 INFN TIFPA）
Event-by-event track imaging of charged particles with an ultrafast plenoptic camera
ETH Zürich（苏黎世联邦理工学院）；Università degli Studi Roma Tre（罗马第三大学）；Istituto Nazionale di Ottica - CNR（意大利国家光学研究所-国家研究委员会）；INFN Sezione di Roma Tre（罗马第三大学意大利国家核物理研究所分部）；Politecnico di Bari（巴里理工大学）；INFN Sezione di Bari（巴里意大利国家核物理研究所分部）；École polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
