Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges; given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.
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）
Tracking charged $b$-hadrons: feasibility study of the use of inner trackers to improve $B^{+}_{(c)}$ reconstruction
INFN Cagliari（意大利国家核物理研究所卡利亚里分部）；Van Swinderen Institute, University of Groningen（格罗宁根大学范斯温德伦研究所）；Nikhef National Institute for Subatomic Physics（荷兰亚原子物理学国家研究所）；Universiteit Maastricht（马斯特里赫特大学）
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）
Unsupervised Particle Tracking with Neuromorphic Computing
INFN（意大利国家核物理研究所）；Università di Padova（帕多瓦大学）；Università di Bologna（博洛尼亚大学）；Luleå University of Technology（吕勒奥理工大学）；Central University of Tamil Nadu（泰米尔纳德邦中央大学）
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）
Particle tracking at high luminosities using a novel reconstruction approach
Indian Institute of Science Education and Research Mohali（印度科学教育研究学院莫哈利分校）；Fr. C. Rodrigues Institute of Technology, Vashi（Vashi C. Rodrigues神父理工学院）；Tata Institute of Fundamental Research, Mumbai（孟买塔塔基础研究所）；Kalinga Institute of Industrial Technology, Bhubaneswar（布巴内斯瓦尔卡林加工业技术学院）
Measurements of underlying-event properties using neutral and charged particles in pp collisions at 900 GeV and 7 TeV with the ATLAS detector at the LHC
