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DOI: 10.5445/IR/1000084837
Veröffentlicht am 30.07.2018

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 conducte ... mehr


Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Publikationstyp Hochschulschrift
Jahr 2018
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-848379
KITopen ID: 1000084837
Verlag Karlsruhe
Umfang VIII, 225 S.
Abschlussart Dissertation
Fakultät Fakultät für Physik (PHYSIK)
Institut Institut für Experimentelle Teilchenphysik (ETP)
Prüfungsdatum 22.06.2018
Referent/Betreuer Prof. U. Husemann
Projektinformation FSP 102 - CMS-Experiment (BMBF, 05H09VKA)
GRK 1694 (DFG, DFG KOORD, GRK 1694/1)
GSC 1085 KSETA (DFG, DFG EXIN, GSC 1085)
Schlagworte PhDthesis, Doktorarbeit, Particle physics, Teilchenphysik, Top physics, Top-Physik, Higgs physics, Higgs-Physik, Data analysis, Datenanalyse, Machine Learning, Neuronale Netze, Neural Networks
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