Optimisation of the CMS ECAL clustering algorithms in view of LHC-Run 3

Abstract

The Run 3 of the Large Hadron Collider (LHC) will be characterised by an enhanced level of noise in the CMS electromagnetic calorimeter (ECAL), caused by the ageing of the photosensors and the loss of crystal transparency due to radiation damage. To face these new conditions, some parameters of the clustering algorithms of the ECAL, namely the Particle-Flow Clustering and SuperClustering algorithms, are tuned to offer optimal performance in terms of signal preservation and noise and pile-up rejection. The methods used for the tuning as well as its performance are presented.