Machine Learning at the Belle II Experiment The Full Event Interpretation and Its Validation on Belle Data /
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. T...
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| Hlavný autor: | |
|---|---|
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham :
Springer International Publishing,
2018.
|
| Vydanie: | 1st ed. 2018. |
| Edícia: | Springer Theses, Recognizing Outstanding Ph.D. Research,
|
| Predmet: | |
| ISBN: | 9783319982496 |
| ISSN: | 2190-5053 |
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| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 003 | SK-BrCVT | ||
| 005 | 20220618120831.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 181229s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319982496 | ||
| 024 | 7 | |a 10.1007/978-3-319-98249-6 |2 doi | |
| 035 | |a CVTIDW11291 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Keck, Thomas. |4 aut | |
| 245 | 1 | 0 | |a Machine Learning at the Belle II Experiment |h [electronic resource] : |b The Full Event Interpretation and Its Validation on Belle Data / |c by Thomas Keck. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a XI, 174 p. 84 illus., 16 illus. in color. |b online resource. | ||
| 490 | 1 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5053 | |
| 500 | |a Physics and Astronomy | ||
| 505 | 0 | |a Introduction -- From Belle to Belle II -- Multivariate Analysis Algorithms -- Full Event Interpretation -- B tau mu -- Conclusion. | |
| 516 | |a text file PDF | ||
| 520 | |a 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. | ||
| 650 | 0 | |a Elementary particles (Physics). | |
| 650 | 0 | |a Quantum field theory. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Sociophysics. | |
| 650 | 0 | |a Econophysics. | |
| 650 | 0 | |a Physical measurements. | |
| 650 | 0 | |a Measurement . | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-98249-6 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE08571 | ||
| 919 | |a 978-3-319-98249-6 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 275787 |d 275787 | ||