Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
This addresses particle tracking for high-energy physics experiments, representing an incremental improvement by adapting existing GNN methods to a new geometry.
The paper tackles particle tracking at the LHC by framing it as a 3D instance segmentation problem and applying Graph Neural Networks (GNNs) in a novel conformal geometry, enabling single-shot track identification and parameter extraction.
3D instance segmentation remains a challenging problem in computer vision. Particle tracking at colliders like the LHC can be conceptualized as an instance segmentation task: beginning from a point cloud of hits in a particle detector, an algorithm must identify which hits belong to individual particle trajectories and extract track properties. Graph Neural Networks (GNNs) have shown promising performance on standard instance segmentation tasks. In this work we demonstrate the applicability of instance segmentation GNN architectures to particle tracking; moreover, we re-imagine the traditional Cartesian space approach to track-finding and instead work in a conformal geometry that allows the GNN to identify tracks and extract parameters in a single shot.