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.

中文翻译：

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使用机器学习的暗能量各向异性应力提示

最近对普朗克数据和高红移类星体的分析表明，可能与平坦的 $\Lambda$ 冷暗物质模型 ($\Lambda$CDM) 存在偏差，其中 $\Lambda$ 是宇宙常数。在这里，我们使用机器学习方法，通过使用最新的宇宙学数据，研究在低红移和高红移下与 $\Lambda$CDM 的任何可能偏差。具体来说，我们应用遗传算法通过重建其状态方程 $w(z)$，物质密度扰动的增长指数 $\gamma(z)$，以独立于模型的方式探索暗能量 (DE) 的性质， DE 扰动的线性 DE 各向异性应力 $\eta_\textrm{DE}(z)$ 和绝热声速 $c_\textrm{s,DE}^2(z)$。我们发现在高红移处 $w(z)$ 与 -1 的 $\sim2\sigma$ 偏差，在 $\sim2 处绝热声速为负。5\sigma$ 水平在 $z=0.1$ 和 $\sim2\sigma$ 的各向异性应力在低红移和高红移时与 1 的统一偏差和 $\sim4\sigma$。这些结果暗示 DE 声速中存在非绝热分量或 DE 各向异性应力的存在，从而暗示可能与 $\Lambda$CDM 模型存在偏差。