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.
Highlights
Tile Calorimeter Principal hadronic calorimeter in ATLAS experiment. Consist of a sampling plastic scintillator/ iron detector. It is divided into three cylindrical sections, referred to as the barrel and extended barrel. Has fixed segmentation often reported in terms of pseudo-rapidity regions.A (Δη = 0.1) BC (Δη = 0.1) D (Δη = 0.2)Tile Long Barrel (LB) | η | < 1.0Tile Extended Barrels (EB) 0.8 < | η | < 1.7
Technical possibility to increase the spatial resolution of the Tile Calorimeter
Generative Adversarial Networks (GAN) is a framework based in game theory
Summary
Technical possibility to increase the spatial resolution of the Tile Calorimeter. Dividing the BC layer into two separate sublayers B and C will provide more information on the longitudinal shower profile. Splitting the A layer along the direction of pseudo-rapidity should help gain more information on the transverse profile of a particle shower
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