Bayesian large-scale structure inference and cosmic web analysis

Abstract : Surveys of the cosmic large-scale structure carry opportunities for building and testing cosmological theories about the origin and evolution of the Universe. This endeavor requires appropriate data assimilation tools, for establishing the contact between survey catalogs and models of structure formation.In this thesis, we present an innovative statistical approach for the ab initio simultaneous analysis of the formation history and morphology of the cosmic web: the BORG algorithm infers the primordial density fluctuations and produces physical reconstructions of the dark matter distribution that underlies observed galaxies, by assimilating the survey data into a cosmological structure formation model. The method, based on Bayesian probability theory, provides accurate means of uncertainty quantification.We demonstrate the application of BORG to the Sloan Digital Sky Survey data and describe the primordial and late-time large-scale structure in the observed volume. We show how the approach has led to the first quantitative inference of the cosmological initial conditions and of the formation history of the observed structures. We then use these results for several cosmographic projects aiming at analyzing and classifying the large-scale structure. In particular, we build an enhanced catalog of cosmic voids probed at the level of the dark matter distribution, deeper than with the galaxies. We present detailed probabilistic maps of the dynamic cosmic web, and offer a general solution to the problem of classifying structures in the presence of uncertainty.The results described in this thesis constitute accurate chrono-cosmography of the inhomogeneous cosmic structure.
Complete list of metadatas

https://tel.archives-ouvertes.fr/tel-01265548
Contributor : Abes Star <>
Submitted on : Monday, February 1, 2016 - 11:33:07 AM
Last modification on : Wednesday, May 15, 2019 - 3:37:08 AM
Long-term archiving on : Friday, November 11, 2016 - 11:10:21 PM

File

these_archivage_2506817o.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01265548, version 1

Citation

Florent Leclercq. Bayesian large-scale structure inference and cosmic web analysis. Instrumentation and Methods for Astrophysic [astro-ph.IM]. Université Pierre et Marie Curie - Paris VI, 2015. English. ⟨NNT : 2015PA066353⟩. ⟨tel-01265548⟩

Share

Metrics

Record views

751

Files downloads

253