Clustering and Tracking in Dense Environments with the ATLAS Inner Tracker for the High-Luminosity LHC

Dense hadronic environments encountered, for example, in the core of high-transverse- momentum jets, present specific challenges for the reconstruction of charged-particle trajectories (tracks) in the ATLAS inner tracking detectors, as they are characterised by a high density of ionising particles. The energy deposits (clusters) left by these particles in the silicon sensors are more likely to merge with increasing particle densities, especially in the innermost layers of the ATLAS silicon-pixel detectors. This has detrimental effects on both the track reconstruction efficiency and the precision with which the track parameters can be measured. The track reconstruction software for the current ATLAS Inner Detector (ID) relies on dedicated machine-learning based algorithms to amend these problems by identifying merged clusters and estimating the positions of the individual sub-clusters. The new Inner Tracker (ITk), which will replace the ID for the High-Luminosity LHC programme, features an improved granularity due to its smaller pixel sensor size, which is expected to reduce cluster merging rates in dense environments. In this note, a comprehensive study of the clustering and tracking performance in dense environments with a recent ITk layout is presented. Different quantities are studied to assess the effects of cluster merging at the cluster-, track-, and jet-level.