Clustering-based Studies on the upgraded ITS of the Alice Experiment

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. The ALICE Collaboration is currently upgrading the entire detector in view of the third run of LHC (2021-2023). One of the key feature is the construction of a low-material budget Inner Tracking System (ITS) based on Monolithic Active Pixel Sensors (MAPS). Each sensor features about 520000 pixels with an adjustable discriminating threshold. Typically the threshold is uniform among the pixels of the sensor. Sensors which shows small areas with higher threshold in our experiment, are named "High Threshold Clusters". In this work, we study the possible effects produced by the high-threshold clusters on the track reconstruction. We first develop a clustering algorithm to find such High Threshold clusters on the chips, hence we excavate some observables of the clusters to do multivariate analysis. Take advantage of the simulation toolbox O2, we introduced the experimental data into simulation and thus certifying the correlation between the cluster parameters and the track reconstruction performance.