论文标题
使用机器学习的暗能量各向异性压力的提示
Hints of dark energy anisotropic stress using Machine Learning
论文作者
论文摘要
在高红移处对Planck数据和类星体的最新分析表明，可能与$λ$冷的暗物质模型（$λ$ CDM）的偏差，其中$λ$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来调查低红移和高红移的$λ$ CDM的任何可能偏差。具体来说，我们通过重建其状态$ W（z）$的方程，以独立方式来探索黑暗能源的性质（de），物质密度扰动的增长指数$γ（z）$，线性de anisotropic de anisotropic posite $η_ $ c_ \ textrm {s，de}^2（z）$ de扰动。我们在高红移时发现$ w（z）$的$ w（z）$的$ \sim2σ$偏差为$ \sim2.5σ$级别为$ z = 0.1 $的$ \sim2.5σ$ evelp，a $ \sim2σ$偏差的各向异性压力在低红色和$ \sim4σ$上与unity的unity偏离。这些结果暗示，要么在声速中存在非绝热成分，要么是各向异性应力的存在，因此暗示了可能与$λ$ CDM模型的偏差。
Recent analyses of the Planck data and quasars at high redshifts have suggested possible deviations from the flat $Λ$ cold dark matter model ($Λ$CDM), where $Λ$ is the cosmological constant. Here we use machine learning methods to investigate any possible deviations from $Λ$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 $γ(z)$, the linear DE anisotropic stress $η_\textrm{DE}(z)$ and the adiabatic sound speed $c_\textrm{s,DE}^2(z)$ of DE perturbations. We find a $\sim2σ$ deviation of $w(z)$ from -1 at high redshifts, the adiabatic sound speed is negative at the $\sim2.5σ$ level at $z=0.1$ and a $\sim2σ$ deviation of the anisotropic stress from unity at low redshifts and $\sim4 σ$ 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 $Λ$CDM model.
