Equivariant Graph Neural Networks for Charged Particle Tracking

• URL: http://arxiv.org/abs/2304.05293v1
• Date: Tue, 11 Apr 2023 15:43:32 GMT
• Title: Equivariant Graph Neural Networks for Charged Particle Tracking
• Authors: Daniel Murnane, Savannah Thais, Ameya Thete
• Abstract summary: EuclidNet is a novel symmetry-equivariant GNN for charged particle tracking. We benchmark it against the state-of-the-art Interaction Network on the TrackML dataset. Our results show that EuclidNet achieves near-state-of-the-art performance at small model scales.
• Score: 1.6626046865692057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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