Savvas Nesseris, Juan Garcia-Bellido
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This information consists of a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity $d_L(z)$ and the angular diameter distance $d_A(z)$ in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density $\om_m(a)$ in the growth rate data, $f\sigma_8(z)$. This information can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state $w(z)$ in the context of FRW models, or the mass radial function $\om_M(r)$ in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, $\eta \equiv \frac{d_L(z)}{(1+z)^2 d_A(z)}$, which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-$\sigma$ deviation from one at redshifts $z\sim 0.5$.
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http://arxiv.org/abs/1205.0364
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