论文标题

$λ$ CDM宇宙中宇宙结构的分析增长功能

Analytical growth functions for cosmic structures in a $Λ$CDM Universe

论文作者

Rampf, Cornelius, Schobesberger, Sonja Ornella, Hahn, Oliver

论文摘要

宇宙流体方程描述了冷暗物质(CDM)的早期重力动力学,暴露于深色能量的均匀成分,即宇宙常数$λ$。流体方程的扰动预测通常假定$λ$对CDM的影响可以通过线性密度波动的精制生长模式$ d $封装。在这里,我们解决了任意高扰动订单,具有{\ it ansatz}的非线性流体方程式,以增加$ d $的流体变量。我们表明,$λ$开始在严格的$ d $扩展中以第五顺序从第五订单开始填充解决方案。通过应用合适的重新召集技术,我们将这些解决方案重新铸造为标准的扰动系列,而不是$ d $,但本质上是初始重力电位作为扩展中的簿记参数。然后,通过在标准扰动理论中使用二阶和三阶的精制生长函数,我们确定了物质功率谱符合一环准确性以及对物质双光谱的领先贡献。我们发现,采用精致的增长功能会影响到后期低于百分之一的总功率和双偏见。但是,对于功率谱而言,我们发现一种特征性尺度依赖性抑制作用,该抑制与大型中微子宇宙学中观察到的相似。因此,我们建议采用精致的增长功能,以减少理论不确定性,以分析相关管道中的数据。

The cosmological fluid equations describe the early gravitational dynamics of cold dark matter (CDM), exposed to a uniform component of dark energy, the cosmological constant $Λ$. Perturbative predictions for the fluid equations typically assume that the impact of $Λ$ on CDM can be encapsulated by a refined growing mode $D$ of linear density fluctuations. Here we solve, to arbitrary high perturbative orders, the nonlinear fluid equations with an {\it Ansatz} for the fluid variables in increasing powers of $D$. We show that $Λ$ begins to populate the solutions starting at the fifth order in this strict $D$-expansion. By applying suitable resummation techniques, we recast these solutions to a standard perturbative series where not $D$, but essentially the initial gravitational potential serves as the bookkeeping parameter within the expansion. Then, by using the refined growth functions at second and third order in standard perturbation theory, we determine the matter power spectrum to one-loop accuracy as well as the leading-order contribution to the matter bispectrum. We find that employing our refined growth functions impacts the total power- and bispectra at a precision that is below one percent at late times. However, for the power spectrum, we find a characteristic scale-dependent suppression that is fairly similar to what is observed in massive neutrino cosmologies. Therefore, we recommend employing our refined growth functions in order to reduce theoretical uncertainties for analysing data in related pipelines.

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