An Analysis of a Dataset from a Prototype Dark Matter Detector and the Design, Simulation and Construction of a New Detector. / Eggleston, Richard.

2016. 236 p.

Research output: ThesisDoctoral Thesis

Unpublished

Documents

Abstract

Dark matter is considered one of the most significant outstanding problems in modern physics. Over the years a number of experimental techniques have been developed with the aim of making a direct detection. The Dark Matter Time Projection Chamber (DMTPC) collaboration uses gasesous CF4 in a time projection chamber (TPC) with charge and imaging readout. The work presented in this thesis represents an analysis on an existing detector with the aim of understanding the backgrounds that are present and improving techniques to reject them from the data. Following this, the design, fabrication and commissioning of a new large-scale detector is described, with focus on reduction of backgrounds from component materials.


The process of analysing the data, results in an improvement to the background rejection methods. A reduction in the overall rate of events passing the selection criteria is observed, verifying the improvement. The upper limit on the spin-dependent dark matter-proton interaction cross-section is comparable to the previous analysis despite having lower total exposure. The value achieved is σpSD = 6.88×10-33 cm2 for a dark matter mass of 145 Gev/c2. A background estimation is presented providing evidence that the remaining candidate events can be attributed to radon progeny recoils, present due to radon emanating from and plated out on the materials that comprise the detector. This finding is used to drive the design of the next detector. This is done by producing a metric which satisfies the desired fiducial volume whilst minimising the surface area contributions of materials. The final field cage design reduces the surface-area to fiducial-volume ratio by a factor of 9.5 compared to the previous detector prototype and a factor of 28 compared to the first prototype (the detector used for the analysis of this thesis). 

Original languageEnglish
QualificationPh.D.
Awarding Institution
Supervisors/Advisors
Award date21 Jun 2016
Publication statusUnpublished - 21 Jun 2016

Research outputs

This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 26577936