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Improvement to the statistical sensitivity of top quark pair production in conjunction with additional heavy flavour jets through multivariate analysis van Rossem, Mackenzie Peter Fulford
Abstract
With the mass of the discovered Higgs-like boson being 125 GeV, this leads to a primary Higgs decay mode to two bottom (b) jets. A precise measurement of top-pair (tt̄) production in conjunction with two additional b-jets is essential to reduce the background uncertainty on the tt̄ + Higgs production cross-section, a direct probe of the Higgs to Yukawa coupling. This thesis attempts to improve on the statistical sensitivity of tt̄ production in conjunction with two additional heavy-flavour jets, using expected sensitivities from 20.3 fb-¹ of pp collision data at √s = 8TeV, collected by the ATLAS detector at the Large Hadron Collider in 2012. This thesis compares multiple multivariate analysis techniques, boosted decision trees and artificial neural networks, in both binary and multi-class classification cases. An overall improvement in precision was seen, from 19.7% uncertainty on the baseline tt̄ + bb̄ measurement based on a fit to the best single variable, to 16.1% uncertainty with the very best multi-class neural network algorithm. This represents a relative improvement of nearly 20% and could thus reduce luminosity needed for a precision measurement of this process.
Item Metadata
Title |
Improvement to the statistical sensitivity of top quark pair production in conjunction with additional heavy flavour jets through multivariate analysis
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2016
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Description |
With the mass of the discovered Higgs-like boson being 125 GeV, this leads to a primary Higgs decay mode to two bottom (b) jets. A precise measurement of top-pair (tt̄) production in conjunction with two additional b-jets is essential to reduce the background uncertainty on the tt̄ + Higgs production cross-section, a direct probe of the Higgs to Yukawa coupling. This thesis attempts to improve on the statistical sensitivity of tt̄ production in conjunction with two additional heavy-flavour jets, using expected sensitivities from 20.3 fb-¹ of pp collision data at √s = 8TeV, collected by the ATLAS detector at the Large Hadron Collider in 2012. This thesis compares multiple multivariate analysis techniques, boosted decision trees and artificial neural networks, in both binary and multi-class classification cases. An overall improvement in precision was seen, from 19.7% uncertainty on the baseline tt̄ + bb̄ measurement based on a fit to the best single variable, to 16.1% uncertainty with the very best multi-class neural network algorithm. This represents a relative improvement of nearly 20% and could thus reduce luminosity needed for a precision measurement of this process.
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Genre | |
Type | |
Language |
eng
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Date Available |
2016-09-08
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0314174
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2016-11
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International