Transformers for Charged Particle Track Reconstruction in High-Energy Physics

Charged particle reconstruction, the identification and characterization of particles from collision data, is fundamental to nearly all research at particle colliders like the Large Hadron Collider (LHC). With the High-Luminosity upgrade (HL-LHC), particle multiplicities will increase substantially, overwhelming traditional track reconstruction algorithms and presenting computational bottlenecks. Here, we introduce a proof of concept for a powerful new method for charged particle reconstruction inspired by state-of-the-art machine learning (ML) approaches in computer vision. Our model leverages transformer neural networks to efficiently filter relevant signals and fully reconstruct particle trajectories, directly tackling the computational complexity that traditional methods face. Evaluated on the widely used TrackML dataset, our approach achieves state-of-the-art tracking efficiency (97%) and a low fake rate (0.7%), requiring just 97 ms to reconstruct on average 1300 particle trajectories from 55,000 detector hits for particles with transverse momentum above 750 MeV. These results represent a significant milestone in both performance and speed, demonstrating a shift toward unified, scalable ML solutions that offer substantial improvements for collider experiments.