Jubna Irakkathil Jabbar2, ; Günter Quast2, ; Florian Bernlochner1, ; Pablo Goldenzweig2 — 1 ; University of Bonn, Germany — 2Karlsruhe Institute of Technology, Germany.

15 March 2021
DPG

Abstract: The simulation of particle showers in electromagnetic calorimeters with high precision is a computationally expensive and time consuming process. Fast simulation of particle showers using generative models have been suggested to significantly save computational resources.In this study, the energy responses of electromagnetic calorimeter for electrons and pion showers are used to train a deep learning generative model.The model is a combination of Wasserstein GAN and Variational Autoencoder.Once the model is trained, the generator of the model is used to generate particle shower simulations providing noise vectors as input.The generated particle showers are cross-checked with the Geant4 showers using various observables.

Note: 15 Min