Combined Higgs analysis and particle identification studies at ATLAS

Wright, Catherine (2010) Combined Higgs analysis and particle identification studies at ATLAS. PhD thesis, University of Glasgow.

Abstract

A statistical combination of the low mass search channels for the Standard Model (SM) Higgs boson at the ATLAS Experiment is presented. It is found that with 1 inverse femtobarn of ATLAS data, the SM Higgs can be excluded between 130 GeV and 190 GeV, at or above the 95% Confidence Level. In the presence of signal, a 5σ observation is expected between 125 GeV and 185 GeV for 10 inverse femtobarns of data. The effect of systematic uncertainties on the discovery and exclusion sensitivities are presented.
The discovery potential of the Higgs plus associated top decay mode of the SM Higgs is assessed and the discovery sensitivity is found to be 1.5σ for 30 inverse femtobarns. It is shown that the use of a neural network can improve the exclusion potential of this search mode by a factor 3, increasing the SM cross-section excluded at 95% CL, with 1 inverse femtobarn of expected ATLAS data, from 14.6 to 4.6σ.
A study of ATLAS particle identification efficiencies is also presented. A tool which applies these efficiencies to the output of the ATLAS fast simulation tool, ATLFast, has been developed. It is shown for isolated electrons from a top-antitop sample that application of the electron identification efficiency improves the agreement between the fast and full simulation from ±10% to ±5%.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Higgs, Log-Likelihood, particle physics, Atlfast, combined Higgs searches
• Subjects: Q Science > QC Physics
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Doyle, Prof A.T.
• Date of Award: 2010
• Depositing User: Miss Catherine Wright
• Unique ID: glathesis:2010-2100
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 10 Nov 2010
• Last Modified: 10 Dec 2012 13:51
• URI: https://theses.gla.ac.uk/id/eprint/2100