Towards the Increase in Granularity for the Main Hadronic ATLAS Calorimeter: Exploiting Deep Learning Methods

Abstract

Abstract During the second phase upgrade program developed for LHC and its experiments, the main hadronic calorimeter of ATLAS (TileCal) will replace completely its readout electronics, but the optical signal pathway and detector will be kept unchanged. During the R&D studies for the upgrade, initial analyses for improving the calorimeter granularity were made. A granularity improvement could be achieved through the introduction of Multi-Anode Photomultiplier Tubes (MA-PMTs) into the calorimeter readout chain, together with applications of image processing algorithms for identifying sub-regions on calorimeter cells. This paper presents the latest results from using a Generative Adversarial Network (GAN) to generate synthetic images, which simulate real images formed in the MA-PMT. After the classification of cell sub-regions, preliminary results show a classification accuracy of more than 98% on the experimental test set.