Cosmoparticle Studentships in Dark Matter research starting October 2017
These studentships, associated with a new cosmoparticle initiative, are co-supervised by staff from different UCL research groups: High Energy Physics (HEP), Astrophysics, and Space & Climate Physics at the Mullard Space Science Laboratory (MSSL). The application procedure is the same as for standard projects; there is no need to apply to both groups to be considered for one of these positions. See also the main list of HEP projects and intructions for applicants. For informal enquiries relating to the Dark Matter projects contact Dr Chamkaur Ghag.
Towards Dark Matter discovery with direct, production, and indirect searches
Supervisors: Chamkaur Ghag (HEP) & Andrew Pontzen (Astrophysics) Discovery of the nature of the Dark Matter, comprising 85% of the matter in the Universe, is internationally recognised as one of the highest scientific priorities of our time. Three complementary techniques are being pursued to shed light on this elusive substance. Direct searches such as the LUX and LZ experiments use highly sensitive ultra-low background detectors in deep underground laboratories to search for rare interactions of galactic Dark Matter in terrestrial targets. Accelerator searches attempt to recreate the conditions of the early Big Bang to produce new Dark Matter particles that would leave characteristic missing energy signatures or evidence of mediator particles in detectors such as ATLAS at the LHC. Indirect searches seek evidence of Dark Matter annihilation decay products from galactic and extra-galactic sources using high precision space-based particle detectors. These are complementary techniques that may bring first detections in the coming years through world-leading experiments. Currently, however, precisely how such signals would relate to one another, and the accuracy with which robust claims of discovery of Dark Matter could be claimed, is unclear. This project will explore the implications of existing results from each on the other fields to constrain the parameter space for Dark Matter models, and define the phase space upon which future limits or positive detections may be collectively mapped. This research is of considerable significance to the field; the interrelation of results from the three search techniques must be well characterised for definitive discovery of Dark Matter.
Towards a better modelling of self-interacting dark matter
Supervisors: Tom Kitching (MSSL) & Chamkaur Ghag (HEP) Dark matter is the transparent material that constitutes the majority of the mass content of the Universe, however its fundamental nature remains a mystery. Cosmologists have attempted to use maps of mass in galaxy clusters, created using gravitational lensing methods, to measure dark matter properties. However the theoretical interpretation of such observations is not well founded on possible dark matter particle candidates. In this PhD we will create macro-physical dark matter models for cosmology that are underpinned by possible dark matter candidates that could be detected at the LHC and with direct detection experiments. The project will start by adapting well understood weakly interacting particle models from plasma physics to the dark matter setting, and apply these to current observations of interacting clusters using Hubble Space Telescope data to measure the self-interaction cross section of dark matter. We will then construct a model for the large-scale distribution of dark matter that will enable wide-field surveys such as Euclid and LSST to measure dark matter particle properties using cross-correlation statistics.
Searching for Dark Matter with Deep Learning
Supervisors: Chamkaur Ghag (HEP) & Jason McEwen (MSSL) Direct Dark Matter search experiments operate highly sensitive detectors in deep underground laboratories, seeking to detect rare and low-energy scatters from Dark Matter particles in our galaxy. The world-leading LUX experiment, based at the Sanford Underground Research Facility, S. Dakota, operates a xenon target that is the most radio-quiet environment on Earth in the hunt for Weakly Interacting Massive Particles (WIMPs). WIMPs are expected to produce characteristic single vertex elastic scattering signatures. The rate of candidate events that satisfy this requirement is low, about 2 per day, and this is consistent with background expectation. However, there are many WIMP and non-WIMP models of Dark Matter that may produce significantly different signatures. LUX triggers about 1 million times per day to record data from completely uncharted electroweak parameter space, potentially containing new physics and non-standard WIMP or non-WIMP Dark Matter signals. The techniques of Machine Learning and Deep Learning present opportunities to analyse this data efficiently, particularly where faint signals with unknown characteristics may be hidden amongst large backgrounds. Developing these techniques could prove to be the key for discovery in the next generation of leading Dark Matter experiment, LZ, presently under construction and set to begin taking data within the lifetime of this project. LZ will examine the bulk of the favoured theoretical parameter space for Dark Matter, uncovering unknown backgrounds never previously encountered and potential signals. This project will develop the routines to analyse the existing rich LUX data for any galactic signals or new backgrounds, and prepare the framework for a robust and rapid interpretation of the data from LZ, be it for the first discovery of WIMPs or any hints of physics Beyond the Standard Model.