Enhanced track reconstruction at the HL-LHC

Abstract

The High-Luminosity Large Hadron Collider (HL-LHC) will substantially increase the charged-particle multiplicity and detector occupancy in the ATLAS and CMS experiments, posing new challenges for the reconstruction of charged-particle tracks. Both experiments are upgrading their tracking detectors, with fully silicon-based systems providing a finer granularity, extended acceptance and updated geometry with respect to the current ones, with the goal to reduce the combinatorial complexity of pattern recognition tasks. At the same time, the more demanding reconstruction environment at the HL-LHC requires algorithms capable of retaining high tracking efficiency while reducing the associated computational cost. To this purpose, both the ATLAS and CMS Collaborations are developing tracking algorithms for offline and online reconstruction, exploiting parallelized, vectorized and heterogeneous architectures. The ATLAS experiment is moving toward an ACTS reconstruction chain, based on the Combinatorial Kalman Filter algorithm. In the CMS experiment, the new High-Level Trigger tracking baseline combines the Patatrack and the Line Segment Tracking algorithms for seed reconstruction, using the mkFit algorithm for track building. These developments yield tracking efficiencies above 85% across the full phase space at high pileup, extending the acceptance to forward and displaced tracks with transverse displacements of up to tens of centimetres, while significantly reducing the reconstruction time. Machine-learning techniques are also being explored at several stages of the reconstruction, including track building. The state-of-the-art performances of the tracking strategies developed by the ATLAS and CMS Collaborations for the HL-LHC are presented, together with prospects of further improvements.