Dark energy and modified theories of gravity
Lima, Nelson Daniel de Aguiar
It is now a consolidated fact that our Universe is undergoing an accelerated expansion. According to Einstein's General Relativity, if the main constituents of our Universe were ordinary and cold dark matter, then we would expect it to be contracting and collapsing due to matter's attractive nature. The simplest explanation we have for this acceleration is in the form of a component with a negative ratio of pressure to density equal to -1 known as cosmological constant, Λ , presently dominating over baryonic and cold dark matter. However, the Λ Cold Dark Matter (Λ CDM) model suffers from a well known fine tuning problem. This led to the formulation of dark energy and modified gravity theories as alternatives to the problem of cosmic acceleration. These theories either include additional degrees of freedom, higher-order equations of motion, extra dimensionalities or imply non-locality. In this thesis we focus on single field scalar tensor theories embedded within Horndeski gravity. Even though there is currently doubt on their ability to explain cosmic acceleration without having a bare cosmological constant on their action, the degree of freedom they introduce mediates an additional fifth force. And while this force has to suppressed on Solar system scales, it can have interesting and observable effects on cosmological scales. Over the next decade there is a surge of surveys that will improve the understanding of our Universe on the largest scales. Hence, in this work, we take several different modified gravity theories and study their impact on cosmological observables. We will analyze the dynamics of linear perturbations on these theories and clearly highlight how they deviate from Λ CDM, allowing to break the degeneracy at the background level. We will also study the evolution of the gravitational potentials on sub horizon scales and provide simplified expressions at this regime and, for some models, obtain constraints using the latest data.