Machine Learning at the Belle II Experiment / Springer Theses (PDF)

Name: Machine Learning at the Belle II Experiment / Springer Theses
Brand: Springer-Verlag GmbH
SKU: 110003886
Price: 117.69 EUR
Availability: OnlineOnly

The Full Event Interpretation and Its Validation on Belle Data (Sprache: Englisch)

Autor: Thomas Keck

Schreiben Sie einen Kommentar zu "Machine Learning at the Belle II Experiment / Springer Theses".

Machine Learning at the Belle II Experiment / Springer Theses, Thomas Keck

Merken

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...

Mehr zum Inhalt Autorenporträt Video

eBook (pdf) 117.69 €

Buch (Gebunden) 128.39 €

sofort als Download lieferbar

Bestellnummer: 110003886

Printausgabe 128.39 €

eBook (pdf) -8% 117.69 €

Download bestellen

Verschenken

58 DeutschlandCard Punkte sammeln

Lastschrift, Kreditkarte, Paypal, Rechnung
Kostenloser tolino webreader

Produktdetails

Produktbeschreibung
Autorenporträt
eBook Hilfe
Biblio. Angaben / Produktdetails

Produktinformationen zu „Machine Learning at the Belle II Experiment / Springer Theses (PDF)“

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.

Autoren-Porträt von Thomas Keck

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.

eBook Hilfe

Informationen und Hilfe zu eBooks hier klicken!

Bibliographische Angaben

Autor: Thomas Keck
2018, 1st ed. 2018, 174 Seiten, Englisch
Verlag: Springer-Verlag GmbH
ISBN-10: 3319982494
ISBN-13: 9783319982496
Erscheinungsdatum: 29.12.2018

Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.

eBook Informationen

Dateiformat: PDF
Größe: 11 MB
Ohne Kopierschutz
Vorlesefunktion

Sprache:

Englisch

Kommentar zu "Machine Learning at the Belle II Experiment / Springer Theses"

Schreiben Sie einen Kommentar zu "Machine Learning at the Belle II Experiment / Springer Theses".

Wie funktionieren Bewertungen?

0 Gebrauchte Artikel zu „Machine Learning at the Belle II Experiment / Springer Theses“

Zustand	Preis	Porto	Zahlung	Verkäufer	Rating