Overview
- Nominated as an outstanding Ph.D. thesis by the RWTH Aachen University, Aachen, Germany
- Received PhD Thesis Award 2019 of the CMS collaboration
- Contributed to first observation of Higgs bosons in association with top quarks
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (9 chapters)
Keywords
About this book
In 1964, a mechanism explaining the origin of particle masses was proposed by Robert Brout, François Englert, and Peter W. Higgs. 48 years later, in 2012, the so-called Higgs boson was discovered in proton-proton collisions recorded by experiments at the LHC. Since then, its ability to interact with quarks remained experimentally unconfirmed.
This book presents a search for Higgs bosons produced in association with top quarks tt̄H in data recorded with the CMS detector in 2016. It focuses on Higgs boson decays into bottom quarks H → bb̅ and top quark pair decays involving at least one lepton. In this analysis, a multiclass classification approach using deep learning techniques was applied for the first time. In light of the dominant background contribution from tt̄ production, the developed method proved to achieve superior sensitivity with respect to existing techniques. In combination with searches in different decay channels, the presented work contributed to the first observations of tt̄H production and H → bb̅ decays.
Authors and Affiliations
Bibliographic Information
Book Title: Search for tt̄H Production in the H → bb̅ Decay Channel
Book Subtitle: Using Deep Learning Techniques with the CMS Experiment
Authors: Marcel Rieger
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-65380-4
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 2021
Hardcover ISBN: 978-3-030-65379-8Published: 26 February 2021
Softcover ISBN: 978-3-030-65382-8Published: 26 February 2022
eBook ISBN: 978-3-030-65380-4Published: 25 February 2021
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XIII, 217
Number of Illustrations: 9 b/w illustrations, 73 illustrations in colour
Topics: Elementary Particles, Quantum Field Theory, Statistics, general, Machine Learning, Particle and Nuclear Physics