Preliminary Performance Study of an Alternative Calorimeter Clustering Solution for Allen in LHCb

Abstract

The LHCb experiment has recently started a new period of data taking after a major upgrade in both software and hardware. One of the biggest challenges has been the migration of the first part of the trigger system into a parallel GPU architecture framework called Allen, which performs a partial reconstruction of most of the LHCb sub-detectors. In Allen, the reconstruction of the Electromagnetic Calorimeter (ECAL) sub-detector is used in many selection algorithms, but its efficiency is currently 10% lower than the full reconstruction performed in the second stage of the trigger. In this work, we present a preliminary performance study of an alternative ECAL reconstruction algorithm implemented in Allen that complements the current algorithm to maximise the reconstruction efficiency and also minimise the impact on the throughput rate.