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Author Keck, Thomas, author
Title Machine learning at the Belle II Experiment : the full event interpretation and its validation on Belle data / by Thomas Keck
Imprint Cham : Springer International Publishing : Imprint: Springer, 2018
book jacket
Descript 1 online resource (xi, 174 pages) : illustrations, digital ; 24 cm
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Series Springer theses, 2190-5053
Springer theses
Note Introduction -- From Belle to Belle II -- Multivariate Analysis Algorithms -- Full Event Interpretation -- B tau mu -- Conclusion
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
Host Item Springer eBooks
Subject Particles (Nuclear physics) -- Computer simulation
Artificial intelligence
Elementary Particles, Quantum Field Theory
Artificial Intelligence
Data-driven Science, Modeling and Theory Building
Measurement Science and Instrumentation
Alt Author SpringerLink (Online service)
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