HybridSeeding: A standalone track reconstruction algorithm for scintillating fibre tracker at LHCb

TL;DR

HybridSeeding is a novel standalone pattern recognition algorithm designed for the LHCb upgrade, significantly improving charged particle reconstruction efficiency while maintaining low fake rates and minimal impact on processing time.

Contribution

It introduces a new pattern recognition method that leverages magnetic field knowledge and detector energy deposits for enhanced particle tracking.

Findings

Increased charged particle reconstruction efficiency.

Maintained low fake rate in track identification.

Minimal addition to real-time data processing time.

Abstract

We describe the Hybrid seeding, a standalone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing.