Machine Learning at the Belle II Experiment

The Full Event Interpretation and Its Validation on Belle Data
1. Auflage 2018
ISBN: 978-3-319-98248-9
Verlag: Springer International Publishing

Buch, Englisch, 174 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 453 g

Reihe: Springer Theses

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 ? 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.

Zielgruppe

Research

Autoren/Hrsg.

Keck, Thomas

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Physik Quantenphysik Teilchenphysik

Inhaltsverzeichnis

Introduction.- From Belle to Belle II.- Multivariate Analysis Algorithms.- Full Event Interpretation.- B tau mu.- Conclusion.

Über Autor(innen)

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.