Thesis ETP-KA/2020-11 BELLE2-PTHESIS-2021-005

Development and Deployment of a Deep Neural Network based Flavor Tagger for Belle II

Jochen Gemmler ; Prof. Michael Feindt ; Prof. Florian Bernlochner

Karlsruhe Institute of Technology Karlsruhe

Abstract: In 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.

Note: Presented on 15 05 2020
Note: PhD

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 Record created 2021-06-14, last modified 2021-06-14

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