Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.790890
Title: Cosmological inference with cosmic shear
Author: Taylor, Peter Llewelyn
ISNI:       0000 0004 8499 9033
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
Over the next decade, data from large Stage IV survey telescopes including Euclid, LSST and WFIRST will provide some of the tightest cosmological constraints. To extract information from these surveys we take advantage of gravitational lensing, an effect predicted by Einstein's general theory of relativity. Gravitational lensing simply refers to the bending of light rays around massive bodies. This causes small changes in the observed ellipticity of galaxies, which is called weak gravitational lensing or | on the largest scales | cosmic shear. By examining these shape distortions over millions, or even billions of galaxies, we can distinguish between alternative cosmological models and measure the fundamental cosmological parameters precisely. While the constraining power of these upcoming data sets will improve by more than an order of magnitude, our statistical methods are not keeping pace. In this thesis I develop three new techniques to take full advantage of next generation surveys. The first of these is a method called k-cut cosmic shear. It allows us to efficiently remove sensitivity to small scales that are too difficult to model accurately due to complicated baryonic physics and nonlinear structure formation. Next I present a method called non-parametric cosmology with cosmic shear. I show how to extract information about the growth of structure and the background expansion of the Universe with no a priori assumption about the underlying cosmological model. This can be used to search for failures of the Lambda-Cold Dark Matter (LCDM) model. Finally I show how to perform inference with full forward models of the cosmic shear data. This approach allows us to seamlessly propagate all astrophysical, theoretical and instrumental systematics into the final parameter constraints, sidestepping complicated issues including the deconvolution of the survey mask and an assumption about the functional form of the likelihood.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.790890  DOI: Not available
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