Equivariant Graph Neural Networks for Charged Particle Tracking
Apr 11, 2023Citations per year
Abstract: (arXiv)
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.Note:
- Proceedings submission to ACAT 2022. 7 pages
- symmetry: rotation
- CERN LHC Coll: upgrade
- benchmark
- neural network
- performance
- data analysis method
- numerical calculations
- programming
- pile-up
References(11)
Figures(4)
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