Cosmic cartography
b'The cosmic origin and evolution is encoded in the large-scale matter distribution\\nobserved in astronomical surveys. Galaxy redshift surveys have become in the\\nrecent years one of the best probes for cosmic large-scale structures. They are\\ncomplementary to other information sources like the cosmic microwave background, since they\\ntrace a different epoch of the\\nUniverse, the time after reionization at which the Universe\\nbecame transparent, covering about the last twelve billion years.\\n Regarding that the Universe is about\\n thirteen billion years old, galaxy\\n surveys cover a huge range of time, even if the sensitivity limitations of the\\n detectors do not permit to reach the furthermost sources in the transparent\\n Universe. This makes galaxy surveys extremely interesting for cosmological evolution studies. \\nThe observables, galaxy position in the sky, galaxy ma\\ngnitude and redshift, however, give an incomplete representation of the real\\nstructures in the Universe, not only due to the limitations and\\nuncertainties in the measurements, but also due to their biased\\nnature. They trace the underlying continuous dark matter field only partially\\nbeing a discrete sample of the luminous baryonic distribution.\\nIn addition, galaxy catalogues are plagued by many complications. Some have a\\nphysical foundation, as mentioned before, others are due to the\\nobservation process. The problem of reconstructing the underlying density\\nfield, which permits to make cosmological studies, thus requires a\\nstatistical approach.\\n\\nThis thesis describes a cosmic cartography project.\\n The necessary concepts, mathematical frame-work, and numerical algorithms are\\nthoroughly analyzed.\\nOn that basis a Bayesian software tool is implemented. The resulting Argo-code allows to \\ninvestigate the characteristics of the large-scale cosmological structure with unprecedented \\naccuracy and flexibility. This is achieved by jointly estimating the large-scale density along \\nwith a variety of other parameters ---such as the cosmic flow, the small-scale peculiar velocity \\nfield, and the power-spectrum--- from the information provided by galaxy redshift\\nsurveys. Furthermore, Argo is capable of dealing with many observational issues like\\nmask-effects, galaxy selection criteria, blurring and noise in a very efficient\\nimplementation of an operator based formalism which was carefully derived for this purpose. \\nThanks to the achieved high efficiency of Argo the application of iterative sampling algorithms \\nbased on Markov Chain Monte Carlo is now possible. This will ultimately lead to a full\\ndescription of the matter distribution with all its relevant parameters like velocities, \\npower spectra, galaxy bias, etc., including the associated uncertainties. Some applications \\nare shown, in which such techniques are used. \\nA rejection sampling scheme is successfully applied to correct for the observational \\nredshift-distortions effect which is especially severe in regimes of non-linear structure \\nformation, causing the so-called finger-of-god effect.\\nAlso a Gibbs-sampling algorithm for power-spectrum determination is presented\\nand some preliminary results are shown in which the correct level and shape of\\nthe power-spectrum is recovered solely from the data.\\n\\nWe present in an additional appendix the gravitational collapse and subsequent neutrino-driven \\nexplosion of the low-mass end of stars that undergo core-collapse Supernovae.\\n We obtain results which are for the first time compatible with the Crab Nebula.'