Future galaxy redshift surveys will make high precision measurements of the cosmic expansion history and the growth of structure which will potentially allow us to distinguish between different scenarios for the accelerating expansion of the Universe. In this thesis we study the nonlinear growth of cosmic structure in different dark energy models, using ultra-large volume N-body simulations. We measure key observables such as the growth of large scale structure, the halo mass function and baryonic acoustic oscillations. We study the power spectrum in redshift space in $\Lambda$CDM and quintessence dark energy models and test predictions for the form of the redshift space distortions. An improved model for the redshift space power spectrum, including the non-linear velocity divergence power spectrum, is presented. We have found a density-velocity relation which is cosmology independent and which relates the non-linear velocity divergence spectrum to the non-linear matter power spectrum. We provide a formula which generates the non-linear velocity divergence $P(k)$ at any redshift, using only the non-linear matter power spectrum and the linear growth factor at the desired redshift. We also demonstrate for the first time that competing cosmological models with identical expansion histories - one with a scalar field and the other with a time-dependent change to Newton's gravitational constant - can indeed be distinguished by a measurement of the rate at which structures grow. Our calculations show that linear theory models for the power spectrum in redshift space fail to recover the correct growth rate on surprisingly large scales, leading to catastrophic systematic errors. Improved theoretical models, which have been calibrated against simulations, are needed to exploit the exquisitely accurate clustering measurements expected from future surveys.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:528623 |
| Date | January 2011 |
| Creators | Jennings, Elise |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/616/ |
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