Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
This work offers an incremental improvement for physicists at the LHC/HL-LHC by reducing the computational burden of event reconstruction.
This paper addresses the combinatorial challenge of track finding in particle physics by incorporating cluster shape information into track seeding algorithms. Using neural networks, the authors demonstrate that cluster shapes can significantly reduce fake combinatorial backgrounds while maintaining high efficiency, as shown with TrackML challenge simulations.
Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.