2018White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)Open access

Multijet Background Estimation for Supersymmetry Searches and Radiation Damage Studies with the ATLAS Detector

H. J. Moss

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Abstract

This thesis documents a data-driven methodology for the estimation of the multijet background in fully hadronic final states with high missing transverse momentum within the ATLAS detector at the Large Hadron Collider. The implementation of this methodology within two analyses searching for evidence of supersymmetric particle production using 36.1 fb−1 of √s = 13 TeV proton-proton collision data is described. \nThe two analyses described in this thesis investigate supersymmetric particle production via the strong interaction both inclusively via the superpartners of the first and second generation quarks and exclusively via the superpartners of the third generation t and b quarks. Both analyses consider the eventual decay of any produced supersymmetric particles to standard model particles and the lightest neutralino, which escapes without detection and is observed as missing momentum in the transverse plane of the detector. \nAdditionally, an investigation into the level of radiation damage received by the ATLAS silicon Semiconductor Tracker is presented with predictions of the level of radiation damage faced over the course of the planned lifetime of the detector.

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This thesis documents a data-driven methodology for the estimation of the multijet background in fully hadronic final states with high missing transverse momentum within the ATLAS detector at the Large Hadron Collider. The implementation of this methodology within two analyses searching for evidence of supersymmetric particle production using 36.1 fb−1 of √s = 13 TeV proton-proton collision data is described. \nThe two analyses described in this thesis investigate supersymmetric particle production via the strong interaction both inclusively via the superpartners of the first and second generation quarks and exclusively via the superpartners of the third generation t and b quarks. Both analyses consider the eventual decay of any produced supersymmetric particles to standard model particles and the lightest neutralino, which escapes without detection and is observed as missing momentum in the transverse plane of the detector. \nAdditionally, an investigation into the level of radiation damage received by the ATLAS silicon Semiconductor Tracker is presented with predictions of the level of radiation damage faced over the course of the planned lifetime of the detector.

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Available abstract

This thesis documents a data-driven methodology for the estimation of the multijet background in fully hadronic final states with high missing transverse momentum within the ATLAS detector at the Large Hadron Collider. The implementation of this methodology within two analyses searching for evidence of supersymmetric particle production using 36.1 fb−1 of √s = 13 TeV proton-proton collision data is described. \nThe two analyses described in this thesis investigate supersymmetric particle production via the strong interaction both inclusively via the superpartners of the first and second generation quarks and exclusively via the superpartners of the third generation t and b quarks. Both analyses consider the eventual decay of any produced supersymmetric particles to standard model particles and the lightest neutralino, which escapes without detection and is observed as missing momentum in the transverse plane of the detector. \nAdditionally, an investigation into the level of radiation damage received by the ATLAS silicon Semiconductor Tracker is presented with predictions of the level of radiation damage faced over the course of the planned lifetime of the detector.

Key concepts: Physics, Particle physics, Neutralino, Large Hadron Collider, Supersymmetry, Nuclear physics, Detector, Atlas (anatomy)

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Multijet Background Estimation for Supersymmetry Searches and Radiation Damage Studies with the ATLAS Detector — Research Paper | ScholarLens