000003314 001__ 3314
000003314 005__ 20221108140256.0
000003314 037__ $$aBELLE2-MTHESIS-2022-039
000003314 041__ $$aeng
000003314 245__ $$aDevelopment of a Graph Neural Network based Tagging Approach for Semi-Inclusive Tagging and Rest of Event Clean Up for the Belle II Experiment
000003314 260__ $$aBonn$$bUniversity of Bonn$$c2022
000003314 300__ $$amult. p
000003314 500__ $$aPresented on 31 03 2022
000003314 502__ $$aMSc$$bBonn, University of Bonn$$c2022
000003314 520__ $$aThis thesis focuses on the development of software tools which shall improve the particle reconstruction at Belle II. The tools are based on a graph neural network, called the DSIT model, and aim to categorize the final state particles of a decay event in order to improve the tagging. This is done as a further development of an already existing semi-inclusive tagging method, the further development being named deep semi-inclusive tagging in this thesis. The main goal is to improve purity and efficiency of the tagging. Later the DSIT model’s capability to clean up the rest of event after the signal 
000003314 700__ $$aProf. Dr. Florian Bernlochner$$edir.
000003314 700__ $$aProf. Dr. Jochen Dingfelder$$edir.
000003314 8560_ $$fitsaklid@uni-bonn.de
000003314 8564_ $$uhttps://docs.belle2.org/record/3314/files/BELLE2-MTHESIS-2022-039.pdf
000003314 980__ $$aTHESIS