Cosmological exploitation of the size function of cosmic voids identifed in the distribution of biased tracers

Cosmic voids are large underdense regions of the Universe that, together with galaxy clusters, filaments and walls, characterise the large-scale structure of the Universe, the so-called cosmic web. Since voids in biased tracers are systematically larger than those identified in the dark matter field, a correction to the dark matter void size function is necessary. The scientific goals of this thesis work are the following: (i) to test the algorithm to clean void catalogues on Λ-cold dark matter (ΛCDM) N-body simulations, (ii) to quantify the accuracy of the theoretical void size function model with dark matter catalogues, (iii) to re-parametrise the theoretical model of the void size function for biased tracers, and (iv) to investigate the cosmological constraining power of cosmic void statistics. For these purposes, we made use of large, high-resolution halo catalogues extracted from ΛCDM N-body simulations. From these catalogues of biased tracers we extracted void catalogues with the Void IDentification and Examination toolkit (VIDE). Then a cleaning procedure is applied to the VIDE void catalogues. In our analyses we found that the theoretical void number density is systematically underpredicted if the effective bias is used to assess the density threshold in the model. We verified this phenomenon by estimating the bias from the ratio between the averaged void density profiles traced by dark matter and biased tracers. In particular, the bias computed with this technique is systematically larger than the linear effective bias of the tracers used to identify the voids. Thus, we used the former to re-parameterise the theoretical size function. Thanks to this new parameterisation, the theoretical void size function is now fully consistent with the result of numerical simulations. Finally, we investigate the effect of varying some of the cosmological parameters used to compute the theoretical void size function