Search for Dileptonic ttH(H → bb) with the ATLAS Detector. / Sowden, Benjamin.


Research output: ThesisDoctoral Thesis




A search for the Higgs boson produced in association with a pair of top quarks (ttH) is presented. The analysis uses 13.2 fb−1 of proton-proton collision data with a centre of mass energy of 13 TeV, collected by the ATLAS experiment at the LHC in 2015 and 2016. The search is optimised for events in which the top quarks decay into electrons or muons and the Higgs boson decays into bottom quarks.

The analysis proceeds by separating the events into categories determined by the number of jets present in the event, and the number of those jets which are identified as originating from bottom hadrons. Multivariate techniques are used in the most signal rich regions to provide optimal separation between the signal and background processes. Finally, a template likelihood fit is performed simultaneously across all the regions, constraining the uncertainties on the analysis and determining the most likely value for the production cross section of ttH.

The search found the best fit production cross section to be 4.6+1.4−1.3(stat)+2.6−1.9(syst) times the standard model predicted result. As such the result does not provide significant evidence for the presence of ttH and a 95% confidence limit was set on the production. It is found that the cross section is disfavoured with values higher than 10.1 times the standard model expected value.

Studies are presented showing the inclusion of event quality variables into the multivariate classifier used in the analysis and the inclusion of colour flow variables into both the multivariate classifier and multivariate reconstruction algorithms used in the analysis. For both sets of variables no significant improvement to the performance of the algorithms was seen.
Original languageEnglish
Awarding Institution
Award date1 Jun 2017
Publication statusUnpublished - 2017
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 28279506