论文标题
机器学习符合CMB温度的红移演变
Machine Learning meets the redshift evolution of the CMB Temperature
论文作者
论文摘要
我们提出了一种独立的和非参数重建，并通过机器学习算法是宇宙微波背景（CMB）温度的红移演化的机器学习算法，该温度是从宽的红移范围$ z \ in \ weft [0,3 \右] $中的$ z \ in \ weft [0,3 \ right] $的，而无需假设任何深色能量模型，即绝对的宇宙或光子数量或光子数量保存。特别是我们使用遗传算法，以避免对初始先验或宇宙学基金模型的依赖性。通过我们的重建，我们在晚期限制了新物理。我们从参数$ \ text {t}（z）= \ text {t} _0（1+z）^{1-β} $，双重关系$η（z）$和cesmicic opacity参数$τ（z）$提供了有关$β$参数的新颖估计值。此外，我们将约束放在变化的良好结构常数$α$上，该结构将在各种物理现象（例如CMB各向异性）中具有签名。总体而言，我们没有发现$1σ$区域内与已建立的$λ\ text {cdm} $模型的偏差的证据，从而证实了其预测潜力。
We present a model independent and non-parametric reconstruction with a Machine Learning algorithm of the redshift evolution of the Cosmic Microwave Background (CMB) temperature from a wide redshift range $z\in \left[0,3\right]$ without assuming any dark energy model, an adiabatic universe or photon number conservation. In particular we use the genetic algorithms which avoid the dependency on an initial prior or a cosmological fiducial model. Through our reconstruction we constrain new physics at late times. We provide novel and updated estimates on the $β$ parameter from the parametrisation $\text{T}(z)=\text{T}_0(1+z)^{1-β}$, the duality relation $η(z)$ and the cosmic opacity parameter $τ(z)$. Furthermore we place constraints on a temporal varying fine structure constant $α$, which would have signatures in a broad spectrum of physical phenomena such as the CMB anisotropies. Overall we find no evidence of deviations within the $1σ$ region from the well established $Λ\text{CDM}$ model, thus confirming its predictive potential.
