Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605528
Title: Towards the DRIFT-III directional dark matter experiment
Author: Sadler, Stephen
ISNI:       0000 0004 5358 7327
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2014
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Abstract:
There exists compelling evidence that baryonic matter constitutes only 15% of the matter budget of the Universe. Results from a diverse range of experiments suggest that the remaining 85% is in the form of weakly interacting particle dark matter, with a particular class of particle, the WIMPs, being favoured on theoretical grounds. Recently, hints of a WIMP signal have appeared at low WIMP mass in several solid-state direct dark matter detectors. However, these appear to be at odds with the exclusion limits from the most sensitive detectors in the world, which employ liquid noble gases as their target media. The DRIFT experiment aims to measure not only the energy, but also the directionality of WIMP-nucleon interactions, which would provide an unambiguous signal of dark matter. The current generation of the detector, the 1 m3 negative ion time projection chamber DRIFT-IId, is currently taking data underground at the Boulby Underground Science Facility. This thesis presents work toward the next generation of the experiment, DRIFT-IIe, which is acting as a technology testbed for the planned 24 m3 DRIFT-III detector. The main background contributor, radon gas, is investigated, and reduced by a factor of 2 through a program of materials screening and substitution. Simplification of the electronics scheme is investigated, and found to be possible with no measurable reduction in directionality or background discrimination. A new gas mixing system for the DRIFT-IIe detector is designed and commissioned, which is more remotely-controllable and incorporates lower-cost components than its predecessor. Finally, a new technique for fiducialising events in the z dimension is presented and a new automated analysis of this data developed, which is shown to improve the efficiency for detecting WIMPs by up to a factor of 3:5.
Supervisor: Spooner, Neil Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.605528  DOI: Not available
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