000002293 001__ 2293
000002293 005__ 20210315152428.0
000002293 037__ $$aBELLE2-TALK-CONF-2021-010
000002293 041__ $$aeng
000002293 100__ $$aJubna Irakkathil Jabbar2, 
000002293 245__ $$aVAE-WGAN and Fast simulation of Electromagnetic Calorimeter Responses
000002293 260__ $$aDPG$$c2021-03-15
000002293 300__ $$a14
000002293 500__ $$a15 Min
000002293 520__ $$aThe simulation of particle showers in electromagnetic calorimeters with high precision is a computationally expensive and time consuming process. Fast simulation of particle showers using generative models have been suggested to significantly save computational resources.In this study, the energy responses of electromagnetic calorimeter for electrons and pion showers are used to train a deep learning generative model.The model is a combination of Wasserstein GAN and Variational Autoencoder.Once the model is trained, the generator of the model is used to generate particle shower simulations providing noise vectors as input.The generated particle showers are cross-checked with the Geant4 showers using various observables.
000002293 700__ $$aGünter Quast2, 
000002293 700__ $$aFlorian Bernlochner1, 
000002293 700__ $$aPablo Goldenzweig2 — 1
000002293 700__ $$aUniversity of Bonn, Germany — 2Karlsruhe Institute of Technology, Germany.
000002293 8560_ $$fjubna.jabbar@kit.edu
000002293 8564_ $$uhttps://docs.belle2.org/record/2293/files/BELLE2-TALK-CONF-2021-010.pdf