000003618 001__ 3618
000003618 005__ 20230526072538.0
000003618 037__ $$aBELLE2-MTHESIS-2023-019
000003618 041__ $$aeng
000003618 100__ $$aIsabel Haide
000003618 245__ $$aFast Simulation and Validation of the Time of Propagation Detector at the Belle II Experiment
000003618 260__ $$aKarlsruhe$$bKarlsruhe Institute of Technology$$c2021
000003618 300__ $$amult. p
000003618 500__ $$aPresented on 16 07 2021
000003618 502__ $$aMSc$$bKarlsruhe, Karlsruhe Institute of Technology$$c2021
000003618 520__ $$aThe generation of Monte Carlo data is very time- and resource-consuming, which will only increase with higher luminosities of the particle physics experiments. The Time of Propagation detector at the Belle II experiment is the current bottleneck regarding simulation time. This thesis is exploring the concept of replacing the TOP simulation with a simulation done through machine learning methods, also called fast simulation. The focus here is on validating a possible fast simulation, thus ensuring correct modeling of the detector. The current simulation done by GEANT4 is evaluated and validation methods to ensure a correct simulation done by machine learning methods are developed. Furthermore, generative adversarial networks and conditional variational autoencoders are implemented and tested on reduced datasets as possible fast simulation algorithms.
000003618 700__ $$aProf. Dr. Günter Quast$$edir.
000003618 700__ $$aProf. Dr. Ulrich Husemann$$edir.
000003618 700__ $$aDr. James Kahn$$edir.
000003618 700__ $$aDr. Pablo Goldenzweig$$edir.
000003618 8560_ $$fisabel.haide@kit.edu
000003618 8564_ $$uhttps://docs.belle2.org/record/3618/files/BELLE2-MTHESIS-2023-019.pdf
000003618 980__ $$aTHESIS