Equivariant Graph Neural Networks for Charged Particle Tracking

Abstract 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…

• NS
  National Science Foundation

  Awards: OAC-1836650, 1836650, DE-AC02-05CH11231
• NE
  National Energy Research Scientific Computing Center

  Awards: 05CH11231, AC02-05CH11231
• OO
  Office of Science

  Awards: AC02-05CH11231, -AC02-05CH11231, DE-AC02, OAC-1836650
• LB
  Lawrence Berkeley National Laboratory

  Awards: DE-AC02-05CH11231, 05CH11231, AC02-05CH11231