Kitaura Joyanes, Francisco Shu (2007): Cosmic cartography: Bayesian reconstruction of the cosmological largescale structure. Dissertation, LMU München: Faculty of Physics 

PDF
Kitaura_Francisco.pdf 6Mb 
Abstract
The cosmic origin and evolution is encoded in the largescale matter distribution observed in astronomical surveys. Galaxy redshift surveys have become in the recent years one of the best probes for cosmic largescale structures. They are complementary to other information sources like the cosmic microwave background, since they trace a different epoch of the Universe, the time after reionization at which the Universe became transparent, covering about the last twelve billion years. Regarding that the Universe is about thirteen billion years old, galaxy surveys cover a huge range of time, even if the sensitivity limitations of the detectors do not permit to reach the furthermost sources in the transparent Universe. This makes galaxy surveys extremely interesting for cosmological evolution studies. The observables, galaxy position in the sky, galaxy ma gnitude and redshift, however, give an incomplete representation of the real structures in the Universe, not only due to the limitations and uncertainties in the measurements, but also due to their biased nature. They trace the underlying continuous dark matter field only partially being a discrete sample of the luminous baryonic distribution. In addition, galaxy catalogues are plagued by many complications. Some have a physical foundation, as mentioned before, others are due to the observation process. The problem of reconstructing the underlying density field, which permits to make cosmological studies, thus requires a statistical approach. This thesis describes a cosmic cartography project. The necessary concepts, mathematical framework, and numerical algorithms are thoroughly analyzed. On that basis a Bayesian software tool is implemented. The resulting Argocode allows to investigate the characteristics of the largescale cosmological structure with unprecedented accuracy and flexibility. This is achieved by jointly estimating the largescale density along with a variety of other parameters such as the cosmic flow, the smallscale peculiar velocity field, and the powerspectrum from the information provided by galaxy redshift surveys. Furthermore, Argo is capable of dealing with many observational issues like maskeffects, galaxy selection criteria, blurring and noise in a very efficient implementation of an operator based formalism which was carefully derived for this purpose. Thanks to the achieved high efficiency of Argo the application of iterative sampling algorithms based on Markov Chain Monte Carlo is now possible. This will ultimately lead to a full description of the matter distribution with all its relevant parameters like velocities, power spectra, galaxy bias, etc., including the associated uncertainties. Some applications are shown, in which such techniques are used. A rejection sampling scheme is successfully applied to correct for the observational redshiftdistortions effect which is especially severe in regimes of nonlinear structure formation, causing the socalled fingerofgod effect. Also a Gibbssampling algorithm for powerspectrum determination is presented and some preliminary results are shown in which the correct level and shape of the powerspectrum is recovered solely from the data. We present in an additional appendix the gravitational collapse and subsequent neutrinodriven explosion of the lowmass end of stars that undergo corecollapse Supernovae. We obtain results which are for the first time compatible with the Crab Nebula.
Item Type:  Thesis (Dissertation, LMU Munich) 

Keywords:  largescale structure, galaxies, powerspectrum, cosmic flow, statistical techniques, numerical inverse methods, image reconstruction techniques 
Subjects:  600 Natural sciences and mathematics > 530 Physics 600 Natural sciences and mathematics 
Faculties:  Faculty of Physics 
Language:  English 
Date Accepted:  20. December 2007 
1. Referee:  White, Simon D. M. 
Persistent Identifier (URN):  urn:nbn:de:bvb:1981208 
MD5 Checksum of the PDFfile:  cc84a075b18ed06c6bcd0c3a82d74788 
Signature of the printed copy:  0001/UMC 16820 
ID Code:  8120 
Deposited On:  11. Mar 2008 13:22 
Last Modified:  16. Oct 2012 08:14 