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arXiv 1809.04854astro-ph.COgr-qchep-ph
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多特征宇宙:大参数空间宇宙学和沼泽地

Multi-feature universe: large parameter space cosmology and the swampland

Deng Wang

摘要:

在宇宙应该是多特征和信息丰富的信念下,我们采用了一种逐模型比较的方法来探索沼泽地常数$c$的可能最大上限。考虑到相互作用的本质暗能量作为比较模型,我们约束的大参数空间相互作用暗能量模型,$Λ$ CDM宇宙学的12个参数的扩展,在目前的观测。我们得到了迄今为止最大的$2σ$($3σ$)束缚,$c\lesssim1.62$ $(1.94)$,这将允许一些暗能量的弦理论模型的存在,如11维超引力与双指数势,$O(16)\times O(16)$杂合弦和一些II型弦紧化。对于具有凹势的暴胀模型,我们发现了$2σ$($3σ$)束缚的$c\lesssim0.13$ $(0.14)$,它仍然与基于弦的期望$c \sim \mathcal{O}(1)$处于强张力中。然而,结合普朗克原始非高斯性与暴胀约束,有趣的是,具有凹势的Dirac-Born-Infeld暴胀给出了$2σ$($3σ$)界$c\lesssim0.53$ $(0.58)$,它现在与沼泽地猜想处于适度的紧张状态。使用贝叶斯证据作为模型选择工具,非常令人惊讶的是,我们的18参数多特征宇宙学比$Λ$ CDM模型更受欢迎。

英文摘要:

Under the belief that the universe should be multi-feature and informative, we employ a model-by-model comparison method to explore the possibly largest upper bound on the swampland constant $c$. Considering the interacting quintessence dark energy as the comparison model, we constrain the large parameter space interacting dark energy model, a 12-parameter extension to the $Λ$CDM cosmology, in light of current observations. We obtain the largest $2σ$ ($3σ$) bound so far, $c\lesssim1.62$ $(1.94)$, which would allow the existences of a number of string theory models of dark energy such as 11-dimensional supergravity with double-exponential potential, $O(16)\times O(16)$ heterotic string and some Type II string compactifications. For inflationary models with concave potential, we find the $2σ$ ($3σ$) bound $c\lesssim0.13$ $(0.14)$, which is still in strong tension with the string-based expectation $c \sim \mathcal{O}(1)$. However, combining Planck primordial non-Gaussianity with inflation constraints, it is interesting that the Dirac-Born-Infeld inflation with concave potential gives the $2σ$ ($3σ$) bound $c\lesssim0.53$ $(0.58)$, which is now in a modest tension with the swampland conjecture. Using the Bayesian evidence as the model selection tool, it is very surprising that our 18-parameter multi-feature cosmology is extremely strongly favored over the $Λ$CDM model.

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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