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Title: Cosmological information from redshift surveys
Author: Ballinger, William Edmund
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1998
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At high redshift the assumption of the incorrect cosmological model can lead to an additional geometric effect which can be confused with redshift distortions. If these can be disentangled, limits can be placed on the cosmological constant Λ in a way which is independent of source evolution. Chapter 3 introduces a detailed power-spectrum model including Λ with linear and nonlinear redshift distortions. The effects of evolution of the bias parameter are considered and a full statistical analysis is performed, showing that the next generation of redshift surveys may be just about capable of putting limits on Λ. A spherical harmonic and spherical Bessel function transform is introduced in chapter 4. Following and refining the analysis of Heavens & Taylor (1995), the effects of linear and nonlinear redshift distortions are modelled along with the effects of incomplete sky coverage and a radial selection function. The equivalence of this method to a conventional Fourier analysis and the advantages this entails are discussed. In chapter 5 the methods are applied to the IRAS 1.2Jy survey and the new PSCz survey. A nonparametric measurement of the shape and amplitude of the real-space (i.e undistorted) power spectrum was introduced and applied to both surveys. In both spectra there is clear evidence for a turnover and Cold Dark Matter models fit fairly well, although there is marginal evidence for a tighter break. In addition, the values β = 1.04 ± 0.3 and β = 0.61 ± 0.17 (marginal errors) were measured respectively for the two surveys, simultaneously with the power spectrum. The latter result - from the superior survey - implies that IRAS galaxies must be biased if a flat Ω0 = 1 universe is required. The likelihood methods discussed and/or used in chapters 3-5 can be computationally expensive to carry out, requiring the repeated inversion of large matrices. In the future, giant datasets could make the task of parameter estimation almost impossible. To deal with this, a method for compressing datasets while retaining information about multiple, correlated parameters is introduced and tested. The method appears to be very promising, and should prove very useful if applied to future surveys.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available