Comparative analysis of model-independent methods for exploring the
  nature of dark energy
  We make a comparative analysis of the various independent methods proposed in
the literature for studying the nature of dark energy, using four different
mocks of SnIa data. In particular, we explore a generic principal components
analysis approach, the genetic algorithms, a series of approximations like
Pad\'e power law approximants, and various expansions in orthogonal
polynomials, as well as cosmography, and compare them with the usual fit to a
model with a constant dark energy equation of state w. We find that, depending
on the mock data, some methods are more efficient than others at distinguishing
the underlying model, although there is no universally better method.
