Cosmology from Cosmic Shear

UoM administered thesis: Phd

  • Authors:
  • Niall Maccrann

Abstract

The observed accelerated expansion of the Universe presents a confounding challenge to our understanding of fundamental physics on cosmic scales, contradicting our experience of the attractive nature of gravity. Despite the accumulating evidence from independent analyses of various observational probes over the last two decades, no satisfactory theoretical explanation for this phenomenon has arisen, allowing the placeholder name `dark energy' to stubbornly linger. I start by describing the concordance model of modern cosmology, and some of the compelling observational evidence that supports it. In the rest of this thesis I explore how we can use `weak gravitational lensing', the subtle distortion of galaxy shapes by the Universe's matter field, to constrain and test cosmological models. Having introduced the theoretical basis of weak lensing, I introduce various systematic effects that make unbiased estimation and interpretation of weak lensing signals so difficult. I do so in the context the Dark Energy Survey (DES), which will provide a significant increase in the volume of weak lensing data available, and thus places stricter requirements on systematic uncertainties than previous datasets. Weak lensing is sensitive to a wide range of systematics, including those arising from incomplete understanding of the instrument and data reduction, and theoretical uncertainties in modelling the signal. I demonstrate improvements in the treatment of various systematic effects by implementation on current data. Firstly, I analyse CFHTLenS data, exploring the tension between this low redshift constraint on the amplitude of matter fluctuations, and the Planck CMB results. I outline further improvements in my analysis of early DES data, presenting cosmological results and comparing to other probes of cosmology.Finally I investigate the theoretically uncertain, systematics-ridden small scales of cosmic shear.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date3 Jan 2017