论文标题
机器学习和精髓的重建和武装的猜想
Machine Learning and cosmographic reconstructions of quintessence and the Swampland conjectures
论文作者
论文摘要
我们使用机器学习（ML）和宇宙学介绍了典型和Swampland猜想（SC）的独立模型重建。特别是，我们演示了理论分析和ML之间的协同作用如何提供有关暗能量和修饰重力性质的关键见解。使用来自宇宙天文纪录器的Hubble参数$ H（Z）$数据，我们发现SC的ML和宇宙学重建与低红移的观察值兼容。最后，包括增长率数据$fσ_8（z）$我们通过两个相图进行了对修改的重力宇宙学的独立模型测试，即$ h-fσ_8$和$η-fσ_8$，其中各向异性应力参数$η$是通过$ e_g $ statistics获得的，这与Gravitational lensitation lensitation lensitation lensitation lensitations Cats Iss相关。虽然在与$λ$ CDM模型的错误中，第一张图是一致的，但第二个图具有$ \ sim2σ$偏离各向异性应力的偏差，而unity的unity在$ z \ sim \ sim 0.3 $中，$ \ sim4σ$ deviation in $ z \ sim 0.9 $，因此指向与总体相关性的偏差，可以进一步测试，可以进一步测试，这可以进一步大规模测试。
We present model independent reconstructions of quintessence and the Swampland conjectures (SC) using both Machine Learning (ML) and cosmography. In particular, we demonstrate how the synergies between theoretical analyses and ML can provide key insights on the nature of dark energy and modified gravity. Using the Hubble parameter $H(z)$ data from the cosmic chronometers we find that the ML and cosmography reconstructions of the SC are compatible with observations at low redshifts. Finally, including the growth rate data $fσ_8(z)$ we perform a model independent test of modified gravity cosmologies through two phase diagrams, namely $H-fσ_8$ and $η-fσ_8$, where the anisotropic stress parameter $η$ is obtained via the $E_g$ statistics, which is related to gravitational lensing data. While the first diagram is consistent within the errors with the $Λ$CDM model, the second one has a $\sim 2σ$ deviation of the anisotropic stress from unity at $z\sim 0.3$ and a $\sim 4σ$ deviation at $z\sim 0.9$, thus pointing toward mild deviations from General Relativity, which could be further tested with upcoming large-scale structure surveys.
