000003577 001__ 3577
000003577 005__ 20230517070235.0
000003577 037__ $$aBELLE2-PTHESIS-2023-010
000003577 041__ $$aeng
000003577 100__ $$aJubna Irakkathil Jabbar
000003577 245__ $$aFast Simulation of Electromagnetic Calorimeter Responses using Deep Learning
000003577 260__ $$aKarlsruhe$$bKarlsruhe Institute of Technology$$c2022
000003577 300__ $$amult. p
000003577 500__ $$aPresented on 29 07 2022
000003577 502__ $$aPhD$$bKarlsruhe, Karlsruhe Institute of Technology$$c2022
000003577 520__ $$aThe monte carlo simulations of particle shower responses in electromagnetic calorimeters demand enormous computational resources and time. The area of deep learning and its application to various fields has made significant progress in the past decade. Fast simulation using deep learning can potentially help in reducing the simulation time. The objective of this thesis is to develop a fast simulation of MC particle shower responses in the electromagnetic calorimeter using deep learning models. Generative networks such as the Wasserstein Generative Adversarial Network, Variational Autoencoders, and their combinations are investigated for the studies.
000003577 700__ $$aProf. Dr. Guenter Quast$$edir.
000003577 700__ $$aProf. Dr. Florian Bernlochner$$edir.
000003577 8560_ $$fisabel.haide@kit.edu
000003577 8564_ $$uhttps://docs.belle2.org/record/3577/files/BELLE2-PTHESIS-2023-010.pdf
000003577 980__ $$aTHESIS