Large-scale growth evolution in the Szekeres inhomogeneous cosmological models with comparison to growth data


الملخص بالإنكليزية

We use the Szekeres inhomogeneous cosmological models to study the growth of large-scale structure in the universe including nonzero spatial curvature and a cosmological constant. In particular, we use the Goode and Wainwright formulation, as in this form the models can be considered to represent exact nonlinear perturbations of an averaged background. We identify a density contrast in both classes I and II of the models, for which we derive growth evolution equations. By including Lambda, the time evolution of the density contrast as well as kinematic quantities can be tracked through the matter- and Lambda-dominated cosmic eras up to the present and into the future. In various models of class I and class II, the growth rate is found to be stronger than that of the LCDM cosmology, and it is suppressed at later times due to the presence of Lambda. We find that there are Szekeres models able to provide a growth history similar to that of LCDM while requiring less matter content and nonzero curvature, which speaks to the importance of including the effects of large-scale inhomogeneities in analyzing the growth of large-scale structure. Using data for the growth factor f from redshift space distortions and the Lyman-alpha forest, we obtain best fit parameters for class II models and compare their ability to match observations with LCDM. We find that there is negligible difference between best fit Szekeres models with no priors and those for LCDM, both including and excluding Lyman-alpha data. We also find that the growth index gamma parametrization cannot be applied in a simple way to the growth in Szekeres models, so a direct comparison of the function f to the data is performed. We conclude that the Szekeres models can provide an exact framework for the analysis of large-scale growth data that includes inhomogeneities and allows for different interpretations of observations. (abridged)

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