Skip to main content
eScholarship
Open Access Publications from the University of California

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

  • Author(s): Ju, X;
  • Murnane, D;
  • Calafiura, P;
  • Choma, N;
  • Conlon, S;
  • Farrell, S;
  • Xu, Y;
  • Spiropulu, M;
  • Vlimant, JR;
  • Aurisano, A;
  • Hewes, J;
  • Cerati, G;
  • Gray, L;
  • Klijnsma, T;
  • Kowalkowski, J;
  • Atkinson, M;
  • Neubauer, M;
  • DeZoort, G;
  • Thais, S;
  • Chauhan, A;
  • Schuy, A;
  • Hsu, SC;
  • Ballow, A;
  • Lazar, A
  • et al.
Abstract

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View