Hints of dark energy anisotropic stress using Machine Learning

摘要: Recent analyses of the Planck data and quasars at high redshifts have suggested possible deviations from flat $\Lambda$ cold dark matter model ($\Lambda$CDM), where is cosmological constant. Here we use machine learning methods to investigate any $\Lambda$CDM both low by using latest data. Specifically, apply Genetic Algorithms explore nature energy (DE) in a independent fashion reconstructing its equation state $w(z)$, growth index density perturbations $\gamma(z)$, linear DE anisotropic stress $\eta_\textrm{DE}(z)$ adiabatic sound speed $c_\textrm{s,DE}^2(z)$ perturbations. We find $\sim2\sigma$ deviation $w(z)$ -1 redshifts, negative $\sim2.5\sigma$ level $z=0.1$ unity $\sim4 \sigma$ redshifts. These results hint towards either presence an non-adiabatic component or stress, thus hinting model.