Equivariant Graph Neural Networks for Charged Particle Tracking

TL;DR

This paper introduces EuclidNet, a symmetry-equivariant graph neural network for charged particle tracking that improves efficiency and performance under high-pileup conditions in high-energy physics experiments.

Contribution

EuclidNet is the first symmetry-equivariant GNN for particle tracking, enforcing rotational symmetry and outperforming non-equivariant models at small scales.

Findings

EuclidNet achieves near-state-of-the-art performance with fewer than 1000 parameters.

It outperforms the non-equivariant Interaction Network on the TrackML dataset.

The model demonstrates improved efficiency in high-pileup conditions.

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.