Measuring Dark Energy Properties with 3D Weak Lensing

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To address the most important questions in cosmology requires the best and most comprehensive data analysis methods. Gravitational lensing, the effect whereby photons are deflected from a straight-line path by the presence of a massive object distorting spacetime locally, is widely accepted to be such a method. This phenomenon, predicted by Einstein, led to the acceptance of General Relativity as our canonical theory gravity in 1919. Now, almost 100 years after this discovery this same phenomenon applied to the lensing of light by large-scale structures of the Universe, as a function of distance or look-back time, enables us to map the 3D dark matter structure of the Universe as well as its expansion history. This combination of lensing information and distance is known as 3D weak lensing this PhD project will build on this foundation, and apply the best analysis methods to the best data available.Development of 3D weak lensing is required for several reasons 1) new theoretical models need to be included, in particular parameterizations of modifications of gravity that extend General relativity to include a potential mechanism to explain dark energy 2) systematic effects, in particular those caused by the alignment of galaxies, that can mimic the weak lensing signal, need to be included. Without these improvements 3D weak lensing cannot reach its full potential.This PhD will apply these new developments the state-of-the-art gravitational lensing data sets: the 154 square degree CFHTLenS survey which is already available and the 1500 square degree ESO KiDS survey that is observing data now. In addition 3D weak lensing-CMB cross correlation statistics will be investigated with an aim to apply this to KiDS and Planck. Finally, this project will build the analysis tools expected to be used in the upcoming ESA Euclid mission in which MSSL has a leading role. In order to ensure a successful PhD this project contains theoretical, simulation and data analysis elements that are flexible such that they can fit with the students skills and expertise.