A new perspective on Dark Energy modeling via Genetic Algorithms

TL;DR

This paper employs Genetic Algorithms to reconstruct cosmological parameters and test fundamental relations in a model-independent way, revealing a potential deviation from expected physics at certain redshifts.

Contribution

It introduces a novel, bias-free method using Genetic Algorithms for reconstructing cosmological functions and estimating errors, applicable to various cosmological data sets.

Findings

Potential 3-sigma deviation from Etherington relation at z~0.5

Model-independent reconstruction of dark energy equation of state

Error estimation method consistent with traditional techniques

Abstract

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 is done by implementing 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, $f\sigma_8(z)$. These quantities 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 $z\sim 0.5$. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.