arXiv 2104.010772021-04-02astro-ph.COgr-qc

冷暗物质偏差和$H_{0}$张力问题的机器学习方法

An approach to cold dark matter deviation and the $H_{0}$ tension problem by using machine learning

Emilio Elizalde, Janusz Gluza, Martiros Khurshudyan

备注：: 14 pages, 4 figures

摘要：

在这项工作中，两个不同的模型，一个与宇宙学常数$Λ$，重子和暗物质（与$ω_{dm} \neq 0$），和另一个与$X$暗能量（与$ω_{de} \neq -1$），重子和暗物质（与$ω_{dm} \neq 0$），进行了研究和比较。使用贝叶斯机器学习分析，两个模型的自由参数的约束条件，获得三个红移范围：$z\in [0,2]$，$z\in [0,2.5]$，和$z\in [0,5]$，分别。对于前两个红移范围，已经存在膨胀率$H(z)$的高质量观测，它们被用于验证拟合结果。此外，扩展范围的$z\in [0,5]$提供了模型参数的预测，验证时，可靠的更高的红移$H(z)$数据。这个学习过程，基于从每个模型的背景动力学产生的膨胀率数据，表明，在宇宙学尺度上，有一个偏离冷暗物质范式，$ω_{dm} \neq 0$，所有三个红移范围。结果表明，这种方法可能有资格作为解决$H_{0}$张力问题。事实上，它暗示了如何在具有相互作用暗能量的宇宙学模型中有效地解决（或至少缓解）这个问题。

英文摘要：

In this work, two different models, one with cosmological constant $Λ$, and baryonic and dark matter (with $ω_{dm} \neq 0$), and the other with an $X$ dark energy (with $ω_{de} \neq -1$), and baryonic and dark matter (with $ω_{dm} \neq 0$), are investigated and compared. Using Bayesian machine learning analysis, constraints on the free parameters of both models are obtained for the three redshift ranges: $z\in [0,2]$, $z\in [0,2.5]$, and $z\in [0,5]$, respectively. For the first two redshift ranges, high-quality observations of the expansion rate $H(z)$ exist already, and they are used for validating the fitting results. Additionally, the extended range $z\in [0,5]$ provides predictions of the model parameters, verified when reliable higher-redshift $H(z)$ data are available. This learning procedure, based on the expansion rate data generated from the background dynamics of each model, shows that, at cosmological scales, there is a deviation from the cold dark matter paradigm, $ω_{dm} \neq 0$, for all three redshift ranges. The results show that this approach may qualify as a solution to the $H_{0}$ tension problem. Indeed, it hints at how this issue could be effectively solved (or at least alleviated) in cosmological models with interacting dark energy.

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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英文摘要：

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