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arXiv 2002.12700astro-ph.COgr-qchep-ph
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机器学习满足CMB温度的红移演化

Machine Learning meets the redshift evolution of the CMB Temperature

Rubén Arjona

摘要:

在不假设任何暗能量模型、绝热宇宙或光子数守恒的情况下,利用机器学习算法,提出了一种与模型无关的非参数重建方法,从宽红移范围$z\in \left[0,3\right]$重建宇宙微波背景(CMB)温度的红移演化.特别是,我们使用遗传算法,避免依赖于初始先验或宇宙学基准模型。通过我们的重建,我们在后期限制了新的物理学。我们提供了新的和更新的估计$β$参数的参数化$\text{T}(z)=\text{T}_0(1+z)^{1-β}$,对偶关系$η(z)$和宇宙不透明度参数$τ(z)$。此外,我们将约束的时间变化的精细结构常数$α$,这将有签名的广泛的物理现象,如CMB各向异性。总体而言,我们没有发现$1σ$区域内偏离完善的$Λ\text{CDM}$模型的证据,从而证实了其预测潜力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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