Machine learning and parallelism in the reconstruction of LHCb and its upgrade

TL;DR

This paper discusses how machine learning and parallel computing techniques enhance real-time track reconstruction in the LHCb experiment, improving efficiency and speed during the upgrade phase.

Contribution

It introduces novel applications of machine learning and parallelism to accelerate and improve the quality of real-time data reconstruction in high-energy physics experiments.

Findings

Reconstruction time was significantly reduced.

Reconstruction efficiency was improved.

Fake rate was decreased.

Abstract

After a highly successful first data taking period at the LHC, the LHCb experiment developed a new trigger strategy with a real-time reconstruction, alignment and calibration for Run II. This strategy relies on offline-like track reconstruction in the high level trigger, making a separate offline event reconstruction unnecessary. To enable such reconstruction, and additionally keeping up with a higher event rate due to the accelerator upgrade, the time used by the track reconstruction had to be decreased. Timing improvements have in parts been achieved by utilizing parallel computing techniques that will be described in this document by considering two example applications. Despite decreasing computing time, the reconstruction quality in terms of reconstruction efficiency and fake rate could be improved at several places. Two applications of fast machine learning techniques are highlighted, refining track candidate selection at the early stages of the reconstruction.