Model selection applied to non-parametric reconstructions of the Dark
  Energy
  The main aim of this paper is to perform a model comparison for
non-parametric reconstructions of the key properties that describe the dark
energy of the Universe i.e. energy density and the equation of state (EoS). We
carry out this process by using a binning and a linear interpolation
methodologies, and on the top of that, we incorporate a correlation function
mechanism. An extension of the two of them was also introduced, where internal
amplitudes are allowed to vary in height as well as in position. The
reconstructions were made with data from the Hubble parameter, Supernovae Type
Ia and Baryon Acoustic Oscillations (H+SN+BAO), all of which span a range from
$z=0.01$ to $z=2.8$. First we perform the parameter estimation for each of the
reconstructions to then provide a model selection through the Bayesian
Evidence. Throughout our process we found a better fit to the data, up to
$4\sigma$ compared to $\Lambda$CDM, and the presence of some interesting
features, i.e. an oscillatory behaviour at late times, a decrease in the dark
energy density component at early times and a transition to the phantom
divide-line in the EoS. To discern these features from noisy contributions, we
include a principal component analysis and found that some of these
characteristics should be taken into account to satisfy observations.
