Imprints of Primordial Non-Gaussianity on Large Scale Structure in the Universe
| Autor: | Hashim, Mahmoud Yousif Ali Wahba |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Popis: | Philosophiae Doctor - PhD Large scale structure in the universe is one of the most important probing tools for cosmological modelling. Assuming the hot big bang model of the universe, the expansion history has undergone two phases of acceleration. The primordial phase which had been considered to be responsible for seeding the structure formation and the late phase driven by the mysterious component of the cosmos, Dark Energy. Early inhomogeneity in the structure formation could though leave a signature on the late time universe. We assume the cosmos to have non-Gaussian initial conditions. Gravitational instability is thought to be responsible for late structure evolution. On local scales, non-linear e ects dissipate primordial signal of non-Gaussianity, however on very large scales, the signal is well preserved. Initial non-Gaussianity introduce a scale dependent signature on the galaxy power spectrum on very large scales. This could be very useful to constrain non-Gaussianity parameter via upcoming large scale structure surveys. In this thesis, we show that on very large scales, interacting dark energy perturbations induce a scale-dependent e ect on the galaxy power spectrum on large scales. This could degenerate with primordial non-Gaussianity signal, though a disentanglement between the two signatures are necessary for observational constraints. On small scales, N-body simulations for standard cosmological models are used to investigate the signature of primordial non-Gaussianity on halo mass function. It has a signi cant e ect on very large halos however it is negligible for small mass halos. Interacting Dark Energy is assumed to have a similar e ect on small scales. We prepare the basis for future work that will combine simulations for Interacting Dark Energy models with non-Gaussian initial conditions. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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