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.

翻译：图神经网络（GNN）在高能物理（HEP）领域因其提升精度与可扩展性的潜力而备受关注。然而，其资源密集的特性与复杂运算推动了对称等变架构的发展。本文提出新型对称等变图神经网络EuclidNet，用于带电粒子追踪。EuclidNet利用对撞事件的图表示，并强制执行关于探测器束流轴线的旋转对称性，从而构建更高效的模型。我们在TrackML数据集（该数据集模拟高亮度大型强子对撞机HL-LHC预期的高堆积环境）上，将EuclidNet与当前最先进的交互网络进行基准测试。结果显示，在小模型规模（参数量<1000）下，EuclidNet实现了接近最优的性能，优于非等变基准模型。本研究为未来在HEP实验中探索更资源高效的粒子追踪GNN模型铺平了道路。