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arXiv 1504.04341astro-ph.COgr-qchep-th
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Horndeski引力在暗物质双谱上的特征

Signatures of Horndeski gravity on the Dark Matter Bispectrum

Emilio Bellini, Raul Jimenez, Licia Verde

摘要:

我们提出了一个详细的研究一般的Horn-deski类模型的二阶物质微扰。作为具有二阶运动方程的最一般的标量张量理论,它包括许多已知的引力和暗能量理论以及作为特定情况的宇宙学常数的广义相对论。这使我们能够估计的主导秩序的暗物质双谱产生的引力不稳定性在后期。我们参数化Bellini和Sawicki(2014)提出的一阶和二阶运动方程的演化,其中理论的自由函数假设与暗能量密度成正比。我们表明,这是不自然的,有大的10%(1%)的偏差的双谱引入更大的~ 30%(~ 5%)的偏差的线性增长率。考虑到测量的线性增长率有更高的信号噪声比双谱测量,这表明,Horndeski模型再现的膨胀历史和线性增长率的预测GR的暗物质双谱内核可以有效地模拟为标准的GR之一。另一方面,不能用偏差来解释的大的双谱偏差的观测将意味着扰动的演化与GR预测的演化强烈不同,或者引力理论是外来的(例如,打破弱等效原理)和/或微调。

英文摘要:

We present a detailed study of second-order matter perturbations for the general Horn- deski class of models. Being the most general scalar-tensor theory having second-order equations of motion, it includes many known gravity and dark energy theories and General Relativity with a cosmological constant as a specific case. This enables us to estimate the leading order dark matter bispectrum generated at late-times by gravitational instability. We parametrize the evolution of the first and second-order equations of motion as proposed by Bellini and Sawicki (2014), where the free functions of the theory are assumed to be proportional to the dark energy density. We show that it is unnatural to have large 10% ( 1%) deviations of the bispectrum introducing even larger ~ 30% (~ 5%) deviations in the linear growth rate. Considering that measurements of the linear growth rate have much higher signal-to-noise than bispectrum measurements, this indicates that for Horndeski models which reproduce the expansion history and the linear growth rate as predicted by GR the dark matter bispectrum kernel can be effectively modelled as the standard GR one. On the other hand, an observation of a large bispectrum deviation that can not be explained in terms of bias would imply either that the evolution of perturbations is strongly different than the evolution predicted by GR or that the theory of gravity is exotic (e.g., breaks the weak equivalence principle) and/or fine-tuned.

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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