Alla ricerca di cosmologie non standard: il ruolo critico degli strumenti numerici

In this thesis we study the realm of non-standard cosmologies, with a particular emphasis on the role of numerical tools in probing beyond the standard Lambda Cold Dark Matter (LCDM) model. Despite the success of the LCDM model, questions persist regarding the true nature of dark matter (DM) and dark energy (DE). We investigate several candidates and mechanisms, including primordial black holes (PBHs) as a form of cold dark matter and the normal branch of Dvali-Gabadadze-Porrati (DGP) gravity as a dynamic DE mode. A significant portion of this thesis is dedicated to studying PBHs, analyzing the constraints placed by cosmic microwave background (CMB) observations on matter accretion into PBHs, and considering the resulting emissions and their effect on the universe’s thermal history. The work explores the complex interplay of accretion processes, outflows, and non-thermal emissions, which introduces considerable theoretical uncertainty in constraining PBH parameters. In addressing the mystery of dark energy, we investigate the nDGP model using galaxy distribution data from the Baryon Oscillation Spectroscopic Survey (BOSS). Through the effective field theory of large-scale structure (EFTofLSS) and perturbative bias expansion, we establish limits on model parameters, showcasing the relationship between these parameters and their degeneracies. Furthermore, the thesis contributes a novel adaptive interpolation tool, leveraging hydrodynamic simulations and the Optimized Kriging technique, to analyze the Lyman-alpha forest data. This allows for enhanced constraints on cosmological models, addressing tensions such as those in the amplitude of the linear power spectrum. The prospective data from the Euclid satellite is anticipated to test the methodologies presented herein, offering a window into the fundamental properties of DM and DE. This thesis underlines the potential of combining astrophysics, astroparticle physics, and cosmology, using numerical simulations and machine learning tools to test a plethora of non-standard cosmologies. The developed techniques stand as a testament to the evolving landscape of cosmological research, advocating for a synergistic approach to unravel the universe’s most enigmatic constituents.