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

During the second phase upgrade program developed for LHC and its experiments, the main hadronic calorimeter of ATLAS (TileCal) will redesign completely its readout electronics, but the optical signal pathway will be kept unchanged. However, there is a technical possibility for improving increasing of the calorimeter granularity through the introduction of Multi-Anode Photomultiplier Tubes (MA-PMTs) on its readout chain. 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 test set.