Authors:
- Nominated as an outstanding Ph.D thesis by the Karlsruhe Institute of Technology, Karlsruhe, Germany
- Includes a comprehensive introduction into the Full Event Interpretation
- Provides detailed results on data recorded by the Belle experiment and simulation studies for the Belle II experiment
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
This book explores how machine learning can be used to improve the efficiency of expensive fundamental science experiments.
The first part introduces the Belle and Belle II experiments, providing a detailed description of the Belle to Belle II data conversion tool, currently used by many analysts.
The second part covers machine learning in high-energy physics, discussing the Belle II machine learning infrastructure and selected algorithms in detail. Furthermore, it examines several machine learning techniques that can be used to control and reduce systematic uncertainties.
The third part investigates the important exclusive B tagging technique, unique to physics experiments operating at the Υ resonances, and studies in-depth the novel Full Event Interpretation algorithm, which doubles the maximum tag-side efficiency of its predecessor.
The fourth part presents a complete measurement of the branching fraction of the rare leptonic B decay “B→tau nu”, which is used to validate the algorithms discussed in previous parts.
Authors and Affiliations
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Institute of Experimental Particle Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Thomas Keck
About the author
Thomas Keck is an experimental high-energy physicists. He obtained his PhD at the Karlsruhe Institute of Technology in 2017. As a member of the Belle and Belle II collaboration he was responsible for the development and implementation of machine learning algorithms in the Belle II Software Framework. In particular, his work was focused on hadronic and semileptonic tagging algorithms, and their application to rare B meson decays. His professional interests include any new technologies in the field of computer science in particular deep learning techniques and their application in physics.
Bibliographic Information
Book Title: Machine Learning at the Belle II Experiment
Book Subtitle: The Full Event Interpretation and Its Validation on Belle Data
Authors: Thomas Keck
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-319-98249-6
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-98248-9Published: 12 January 2019
eBook ISBN: 978-3-319-98249-6Published: 29 December 2018
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XI, 174
Number of Illustrations: 68 b/w illustrations, 16 illustrations in colour
Topics: Elementary Particles, Quantum Field Theory, Artificial Intelligence, Data-driven Science, Modeling and Theory Building, Measurement Science and Instrumentation