000002423 001__ 2423
000002423 005__ 20210614113244.0
000002423 037__ $$aBELLE2-PTHESIS-2021-005
000002423 041__ $$aeng
000002423 088__ $$aETP-KA/2020-11
000002423 100__ $$aJochen Gemmler
000002423 245__ $$aDevelopment and Deployment of a Deep Neural Network based Flavor Tagger for Belle II
000002423 260__ $$aKarlsruhe$$bKarlsruhe Institute of Technology$$c2020
000002423 300__ $$a115
000002423 500__ $$aPresented on 15 05 2020
000002423 502__ $$aPhD$$bKarlsruhe, Karlsruhe Institute of Technology$$c2020
000002423 520__ $$aIn this thesis a flavor tagging algorithm based on these Deep Learning methods is developed and investigated. It is essential for the usage of this algorithm to not only evaluate its performance on generated Monte Carlo data, but also its actual performance on experimental data. Potential differences have to be investigated and quantified to shed light on the accuracy and reliability of the algorithm. The algorithm is designed for the Belle II experiment, and is validated on existing Belle data. The effective tagging efficiency of the developed algorithm is determined on a selection of reconstructed B0 → D∗−π+ and B− → D0 π− decays and compared to the efficiency of an alternative, more traditional approach. Although the chosen hadronic decays have lower statistics than semi-leptonic decays, the reconstruction is much cleaner, and a lower cross-feed between signal side and tag side is expected.
000002423 700__ $$aProf. Michael Feindt$$edir.
000002423 700__ $$aProf. Florian Bernlochner$$edir.
000002423 8560_ $$fpablo.goldenzweig@kit.edu
000002423 8564_ $$uhttps://docs.belle2.org/record/2423/files/BELLE2-PTHESIS-2021-005.pdf
000002423 980__ $$aTHESIS