Hints of dark energy anisotropic stress using Machine Learning
  Recent analyses of the Planck data and quasars at high redshifts have
suggested possible deviations from the flat $\Lambda$ cold dark matter model
($\Lambda$CDM), where $\Lambda$ is the cosmological constant. Here we use
machine learning methods to investigate any possible deviations from
$\Lambda$CDM 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 $\gamma(z)$,
the linear DE anisotropic stress $\eta_\textrm{DE}(z)$ and the adiabatic sound
speed $c_\textrm{s,DE}^2(z)$ of DE perturbations. We find a $\sim2\sigma$
deviation of $w(z)$ from -1 at high redshifts, the adiabatic sound speed is
negative at the $\sim2.5\sigma$ level at $z=0.1$ and a $\sim2\sigma$ deviation
of the anisotropic stress from unity at low redshifts and $\sim4 \sigma$ 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 $\Lambda$CDM model.
