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Measurement of the tt̄+≥1b-jet cross section using novel multivariate analysis techniques at the CMS experiment

Harrendorf, Marco Alexander

Abstract (englisch):

This thesis provides a measurement of the signal
strength and cross section for the production of a top quark-antiquark pair in association
with one or more jets with a bottom hadron (tt̄+≥1b-jet), while employing neural networks
as multivariate analysis method in such a measurement for the first time. In addition to the
neural network analysis the tt̄+≥1b-jet signal strength and cross section is also determined
by using a simpler B-jet multiplicity based analysis acting as a baseline analysis and a linear
discriminant based analysis, which was used as a cross check of the neural network based
analysis.
Furthermore, a simultaneous measurement of the tt̄+bb̄ signal strength and cross section,
the tt̄+2b signal strength and cross section, and the tt̄+b signal strength and cross section
is presented in this thesis. These three processes are subsummed under the term tt̄+≥1b-jet
processes.
In similar fashion, a simultaneous measurement of the tt̄+≥1b-jet signal strength and cross
section and the signal strength and cross section of the associated production of top quark-
antiquark pairs and a Higgs boson (tt̄H) was conducted and its results are given.
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Volltext §
DOI: 10.5445/IR/1000084837
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Publikationstyp Hochschulschrift
Publikationsjahr 2018
Sprache Englisch
Identifikator urn:nbn:de:swb:90-848379
KITopen-ID: 1000084837
Verlag Karlsruher Institut für Technologie (KIT)
Umfang VIII, 225 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Physik (PHYSIK)
Institut Institut für Experimentelle Teilchenphysik (ETP)
Prüfungsdatum 22.06.2018
Projektinformation FSP 102 - CMS-Experiment (BMBF, 05H09VKA)
GRK 1694 (DFG, DFG KOORD, GRK 1694/1)
GSC 1085 KSETA (DFG, DFG EXIN, GSC 1085)
Schlagwörter PhDthesis, Doktorarbeit, Particle physics, Teilchenphysik, Top physics, Top-Physik, Higgs physics, Higgs-Physik, Data analysis, Datenanalyse, Machine Learning, Neuronale Netze, Neural Networks
Referent/Betreuer Husemann, U.
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