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Jeu. 23/01/2025 14:00 Salle des Séminaires, Bâtiment 21, Etage 4 (à confirmer) Séminaire
CELERIER Marie-noelle (Observatoire Paris-Meudon)
An inhomogeneous cosmological solution of GR in the era of precision cosmology: the Szekeres model (LUPM/Particules, Astroparticules, Cosmologie : Théorie)
Sommaire:
The LambdaCDM model, together with its linear and higher order perturbations, constitute the roots of contemporary cosmology allowing us to understand roughly the evolution and the geometry of our Universe. The following thoughts inspire us to go further. First, The Universe homogeneity and isotropy are only averaged properties valid at scales which grow as structures with larger redshifts are discovered. The advent of precision cosmology results in the emerging or in the increasing of tensions, even of anomalies, owing to the mismatch between predictions extrapolated since the early Universe and observations of the late Universe realized with increased precision. General Relativity offers a mean to solve these inconsistencies. The Szekeres model is an exact solution of Einstein's equations, inhomogeneous and without symmetry, able to represent the matter/cosmological constant dominated region of our Universe, since its gravitational source is a pressureless fluid and since the cosmological constant can be incorporated in its equations. One among its main advantages is that it includes the homogeneous and isotropic Friedmann solution as a limiting case. It is therefore able to reproduce naturally the homogeneity/inhomogeneity transition at the scale where its defining functions reach the values of the standard cosmological parameters. The most robust predictions of the early Universe cosmology are therefore maintained. During this seminar, the Szekeres solution will be presented together with its main properties necessary for a cosmological use. The different observations which will be of use to constrain the parameter-functions determining the Szekeres model of Universe valid at a given precision will de described and commented. The perspectives opened by neural networks to complete this analysis of huge data sets will be discussed. Pour plus d'informations, merci de contacter Teixeira E.
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