Speaker
Simon Patrik Schnake
(CMS (CMS Fachgruppe Searches))
Description
The simulation of particle showers in calorimeters is a computational demanding process. Deep generative models have been suggested to replace these computations. One of the complexities of this approach is the dimensionality of the data produced by high granularity calorimeters. One possible solution could be progressively growing the GAN to handle this dimensionality. In this study, electromagnetic showers of a (25x25x25) calorimeter in the energy range of 10 - 510 GeV are used to train generative adversarial networks. The resolution of the calorimeter data is increased while training. First results of this approach are shown.
Primary author
Simon Patrik Schnake
(CMS (CMS Fachgruppe Searches))
Co-authors
Dirk Kruecker
(CMS (CMS Fachgruppe Searches))
Kerstin Borras
(DESY and RWTH Aachen University)
Florian Rehm
(CMS (CMS Fachgruppe Searches))
Sofia Vallecorsa
(CERN)
