In this report we discuss appropriate strategies for the tracking of charged particles in the limit of zero curvature. The suggested approach avoids special treatments and precision issues that frequently arise in that limit. We provide explicit expressions for transport, refitting and vertexing in regions where magnetic field inhomogeneities or detector interaction effects can be approximately ignored.
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）
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）
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）
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）
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
