High Pileup Particle Tracking with Object Condensation

Traditional charged particle tracking algorithms at the Large Hadron Collider (LHC) are based on the combinatorial Kalman filter. However, this class of algorithms exhibits sub-optimal scaling with respect to pileup, rendering tracking a bottleneck for future experiments such as the High Luminosity LHC (HL-LHC) [1]. This has prompted research into tracking algorithms leveraging graph neural networks (GNNs) or similar machine learning (ML) architectures demonstrating improved computational scaling. Recent results have confirmed that GNN-based algorithms can indeed achieve linear scaling with pileup [2, 3]. The majority of GNN approaches adopt an edge classification (EC) approach to tackle the tracking problem. Given an initial graph that connects all hits that potentially belong to the same particle, a GNN is trained to remove edges that connect hits belonging to different particles.