Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614437
Title: The character of Dark Matter
Author: Davis, Jonathan Henry Maynard
ISNI:       0000 0004 5366 5882
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2014
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Abstract:
From galaxies, to clusters, to the Cosmic Microwave Background, there is strong gravitational evidence that the matter content of the Universe is not restricted to the particles of the Standard Model. Specifically, observations indicate that there must also be a large relic population of non-luminous Dark Matter. However, the character of this Dark Matter remains unknown: in particular, to what extent does it interact with the particles of the Standard Model, and with itself, through non-gravitational means? We seek to answer this question in this thesis. We first present constraints on the interaction of Dark Matter with quarks, through an analysis of data from the XENON100 and CoGeNT Direct Detection experiments. In order to do so, we develop a Bayesian technique, which aims to maximise the amount of information we can extract from the data. After this, we discuss potential constraints on the charge of Dark Matter due to its interactions with galactic magnetic fields, and the potential for constraints on its self-annihilation cross section from Cosmic Ray data. We also consider Dark Photons, which partner Dark Matter in many models, and place bounds on their couplings to quarks using the quark-gluon plasma, produced in heavy-ion collisions. We place emphasis on a multi-scale approach and on the robust statistical treatment of Dark Matter data. Our main scientific result comes from the analysis of CoGeNT data, where we show that there is less than one sigma evidence for DM recoils, in contrast to previous claims. We show that the ‘region of interest’ derived in previous analyses, is the result of a bias in the analysis from a particular choice of functional fit for the energy-dependence of the fraction of bulk events. When we account for this bias the preference for Dark Matter vanishes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.614437  DOI: Not available
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