Zhang, Zhongyi: Dark Matter Phenomenology at Colliders. - Bonn, 2020. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-57633
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-57633,
author = {{Zhongyi Zhang}},
title = {Dark Matter Phenomenology at Colliders},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2020,
month = feb,

note = {Dark Matter candidates are required in physics beyond the Standard Model. The preferable models should not only explain the observed phenomena, but also be testable by experiments including direct detection, indirect detection, and collider searches. In this thesis, we focus on simplified Dark Matter models, and try to combine the experiments at colliders with non–collider data to scrutinize such models. On the theoretical side, the models considered in this thesis are the simplified Dark Matter models containing spinor or scalar Dark Matter particles and a massive vector mediator, which couples to both Dark Matter and Standard Model particles including quarks and leptons. On the experimental side, recent collider analyses related to the simplified Dark Matter models are mainly from ATLAS and CMS collaborations at LHC. Nevertheless, we also apply some old LEP data at e+e- collider to probe the parameter region where the LHC data are insensitive. The analyses used in this thesis cover collider signatures with mono–jet + missing energy, di–jet + missing energy, di–jet resonance, 4–jet, di–lepton + missing energy, and multi–lepton final states. However, the published analyses are not always well designed for the selected models related to Dark Matter. Therefore, we further study the optimization for the signal–to–background ratio and the selection efficiency for Dark Matter models, in order to improve the results from published analyses through both the cut based methods and the Machine Learning based algorithms.},
url = {https://hdl.handle.net/20.500.11811/8292}

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