Hints of dark energy anisotropic stress using machine learning

Abstract

Recent analyses of the Planck data and quasars at high redshifts have suggested possible deviations from the flat Λ cold dark matter model (ΛCDM), where Λ is the cosmological constant. Here we use machine learning methods to investigate any possible deviations from ΛCDM at both low and high redshifts by using the latest cosmological data. Specifically, we apply the Genetic Algorithms to explore the nature of dark energy (DE) in a model independent fashion by reconstructing its equation of state w(z), the growth index of matter density perturbations γ(z), the linear DE anisotropic stress ηDE(z) and the adiabatic sound speed cs,DE2(z) of DE perturbations. We find a ∼ 2σ deviation of w(z) from -1 at high redshifts, the adiabatic sound speed is negative at the ∼ 2.5σ level at z = 0.1 and a ∼ 2σ deviation of the anisotropic stress from unity at low redshifts and ∼ 4σ at high redshifts. These results hint towards either the presence of an non-adiabatic component in the DE sound speed or the presence of DE anisotropic stress, thus hinting at possible deviations from the ΛCDM model.