Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.788281
Title: Dark Matter and how to find it : a search for low-mass leptophobic Dark Matter mediators and the development of mass-decorrelated jet taggers with the ATLAS experiment
Author: Søgaard, Andreas
ISNI:       0000 0004 8497 9390
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2019
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Abstract:
A search for low-mass leptophobic Dark Matter (DM) mediator particles in 36 fb-1 of pp collision data at √s = 13 TeV collected by the ATLAS experiment is presented. The search is performed in final states where the mediator decay into a quark pair is reconstructed as a single, large-radius jet produced in association with a photon or a jet. No deviations from the Standard Model expectation are observed, and limits are placed on the production cross-section of leptophobic mediator particles and their coupling to quarks for mediator masses between 100 and 220 GeV. At the time of publication, this result constituted the lowest limits on leptophobic DM mediator masses for high-mass DM particles reported by ATLAS. Adversarial neural networks (ANN) are presented as a way to train jet taggers which decorrelates them from the invariant mass of the jet. An extensive study of five different approaches to constructing mass-decorrelated jet taggers is presented. The ANN tagger is found to provide the largest QCD multijet rejection at similar levels of mass-decorrelation.
Supervisor: Leonidopoulos, Christos ; Clark, Philip Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.788281  DOI: Not available
Keywords: Standard Model ; Dark Matter ; mediator particle ; high-energy proton-proton collisions ; DM mediator particles ; DM mediator decay ; machine learning ; jet identification observables
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