Testing the methods to reconstruct and model the Baryonic Acoustic Oscillations of different tracers using N-body simulations

The accelerated expansion of the Universe and the nature of the Dark Energy are still open questions in cosmology. One of the most powerful ways to investigate these issues is to map the large-scale structure of the Universe, to constrain its expansion history and growth of structures. In particular, baryon acoustic oscillations (BAO) occurred at recombination make a peak in the correlation function of galaxies at the characteristic scale of the sound horizon (a sufficiently large scale to “protect” the signal from strong non-linearities), or alternatively a series of oscillations in the power spectrum. Since the sound horizon can be estimated with a great precision from the position of the first peak in the angular power spectrum of the Cosmic Microwave Background (which has the same physical origin of BAO, oscillations of the baryons-photons plasma), the BAO peak in the correlation function can be used as a standard ruler, providing paramount cosmological information. The aim of this thesis is to systematically test and possibly improve the state-of- the-art statistical methods to model the BAO peak, taking into account the non-linear evolution of matter overdensities, redshift-space distortions and the bias of cosmic tracers. To do that, we analyse mock samples of galaxies, quasars and galaxy clusters extracted from one of the largest available cosmological hydrodynamical simulations. We extract cosmological constraints from the BAO peak through different statistical tools in the redshift range 0.2 < z < 2. Although the BAO peak is at large scales, non-linear growth and galaxy peculiar velocities make the BAO signal smoothed and broader with respect to linear predictions, especially at low redshifts. A possible method to overcome these issues is the so-called reconstruction of the density field: one of the primary goals of this work is to implement a reconstruction method, to check its performances as a function of sample selections and redshift.