Beyond 4D tracking: using cluster shapes for track seeding. (3rd May 2021)

• Record Type: Journal Article
• Authors: Fox, P.J.; Huang, S.; Isaacson, J.; Ju, X.; Nachman, B.
• Is Part Of: Journal of instrumentation. Volume 16:Number 5(2021)
• Publication Date: 2021-05-03
• Subjects: Gaseous imaging and tracking detectors -- Particle tracking detectors -- Particle tracking detectors (Solid-state detectors) -- Pattern recognition, cluster finding, calibration and fitting methods; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/16/05/P05001
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: 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 pixel-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.