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Title: Bayesian methods in cosmology
Author: Bridle, S. L.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2001
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This thesis is concerned with the amount and distribution of dark matter in the universe and makes use of Bayesian techniques to extract maximal information. I have been using two independent approaches. I have (i) compared and combined cosmological parameter estimates from various cosmological probes and (ii) I have developed a method for estimating the mass distribution in clusters of galaxies using gravitational lensing. The first approach tests cosmological theories and estimates the cosmological parameters, including the total amount of matter. The second produces maps of the dark matter in the largest gravitationally bound structures in the universe. Chapter 3 is also published as 'Cosmological Parameters from Cluster Abundances, CMB and IRAS' by Bridle et al. 1999 (MNRAS, 310, 565). Chapter 4 is also available as 'Cosmological Parameters from Velocities, CMB and Supernovae' by Bridle et al. 2000 (astro-ph/0006170, MNRAS accepted). Chapter 5 contains the work presented in Lahav, Bridle, Hobson, Lasenby and Sodré Jr. 2000 (MNRAS, 315, L45) entitled 'Bayesian 'Hyper-Parameters' Approach to Joint Estimation: the Hubble Constant from CMB Measurements' and further demonstrates the properties of this approach by applying it to toy models. Part II concerns maximum-entropy reconstruction of mass distributions from weak gravitational lensing data and consists of two chapters. Chapter 6 sets out the basic method, also published in 'A maximum-entropy method for reconstructing the projected mass distribution of gravitational lenses' by Bridle et al. 1998 (MNRAS, 299, 895). Chapter 7 details extensions to this work, also shortly to become available in Bridle et al. 2000, entitled 'Maximum-Entropy Reconstruction of Gravitational Lenses using Shear and/or Magnification Data'. Future directions are suggested in the conclusions to each part and in the Concluding Remarks.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available