论文标题

挑出修改后的重力参数和数据集揭示了普朗克和镜头之间的二分法

Singling out modified gravity parameters and datasets reveals a dichotomy between Planck and lensing

论文作者

Garcia-Quintero, Cristhian, Ishak, Mustapha

论文摘要

在宇宙学量表上测试一般相对性(GR)的重要途径通常是通过约束在爱因斯坦扰动方程中添加的修饰重力(MG)参数来完成的。到目前为止,大多数研究都对成对的MG参数进行了分析,但是在这里,我们一次探索一个参数的约束,同时将另一个参数固定为GR值。这使我们能够分析各种模型,同时从数据中受益于更强的约束功率。我们还探索了哪些特定数据集与GR处于紧张状态。我们发现具有($μ= 1 $,$η$)和($μ$,$η= 1 $)的模型展示了3.9- $σ$和3.8- $σ$在使用planck18+sne+bao时,而($μ$,$μ$,$η$)显示3.4-- $σ$。对于固定的gr $σ$,我们没有发现与GR的GR张力。使用贝叶斯模型选择分析,我们发现某些单参数MG模型在使用除Planck CMB镜头和DES数据以外的所有数据集组合时,在$λ$ CDM上受到适度的青睐。也就是说,Planck18显示出与GR的中等张力,仅在添加RSD,SNE或BAO组合时会增加。但是,添加透镜减小或消除了这些紧张局势,这可以归因于镜头在约束Mg参数$σ$中的能力。在使用GR进行一致性测试时,发现两个数据集的总体数据集具有二分法,这可能是由于系统的效果,缺乏约束功率或建模。这些发现值得进一步调查,使用正在进行的和将来的调查中的更精确的数据。

An important route to testing General Relativity (GR) at cosmological scales is usually done by constraining modified gravity (MG) parameters added to the Einstein perturbed equations. Most studies have analyzed so far constraints on pairs of MG parameters, but here, we explore constraints on one parameter at a time while fixing the other at its GR value. This allows us to analyze various models while benefiting from a stronger constraining power from the data. We also explore which specific datasets are in tension with GR. We find that models with ($μ=1$, $η$) and ($μ$, $η=1$) exhibit a 3.9-$σ$ and 3.8-$σ$ departure from GR when using Planck18+SNe+BAO, while ($μ$, $η$) shows a tension of 3.4-$σ$. We find no tension with GR for models with the MG parameter $Σ$ fixed to its GR value. Using a Bayesian model selection analysis, we find that some one-parameter MG models are moderately favored over $Λ$CDM when using all dataset combinations except Planck CMB Lensing and DES data. Namely, Planck18 shows a moderate tension with GR that only increases when adding any combination of RSD, SNe, or BAO. However, adding lensing diminishes or removes these tensions, which can be attributed to the ability of lensing in constraining the MG parameter $Σ$. The two overall groups of datasets are found to have a dichotomy when performing consistency tests with GR, which may be due to systematic effects, lack of constraining power, or modelling. These findings warrant further investigation using more precise data from ongoing and future surveys.

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