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arXiv 2201.04193astro-ph.COgr-qc
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具有成团暗能量的暗物质晕的潮汐维里化

Tidal virialization of dark matter haloes with clustering dark energy

Francesco Pace, Carlo Schimd

摘要:

我们扩展了佩斯等人的分析,JCAP,2019,060,通过考虑扩展球形坍缩模型中的维里化过程来聚类暗能量模型,即,解释了暗能量波动与标准方法不同的是,这里的维里化自然是通过适当地模拟潮汐相互作用引起的剪切和旋转引起的球形偏差来实现的。我们研究了七个团簇动力学暗能量模型中位力超密度$Δ_\mathrm{vir}$的时间演化,并将结果与$Λ$ CDM模型和相应的光滑暗能量模型进行了比较.考虑到所有适当的修正,我们推导出的收敛峰鲁宾天文台LSST和欧几里得弱透镜调查,Sunyaev-Zel'dovich峰的西蒙天文台一样的CMB调查,和X射线峰的eROSITA一样的调查。尽管聚集暗能量模型和光滑暗能量模型之间的$Δ_\mathrm{vir}$存在微小差异,但由于这些调查覆盖的体积很大,七个聚集暗能量模型中有五个可以从统计上与$Λ$ CDM区分开来。暗能量涨落的贡献是不可忽略的,特别是对于Chevallier-Polarski-Limber和Albrecht-Skordis模型,只要仪器配置提供高的信噪比。这些结果几乎与潮汐维里化模型无关。

英文摘要:

We extend the analysis of Pace et al., JCAP, 2019, 060, by considering the virialization process in the extended spherical collapse model for clustering dark-energy models, i.e., accounting for dark-energy fluctuations. Differently from the standard approach, here virialization is naturally achieved by properly modelling deviations from sphericity due to shear and rotation induced by tidal interactions. We investigate the time evolution of the virial overdensity $Δ_\mathrm{vir}$ in seven clustering dynamical dark energy models and compare the results to the $Λ$CDM model and to the corresponding smooth dark-energy models. Taking into account all the appropriate corrections, we deduce the abundance of convergence peaks for Rubin Observatory-LSST and Euclid-like weak-lensing surveys, of Sunyaev-Zel'dovich peaks for a Simon Observatory-like CMB survey, and of X-ray peaks for an eROSITA-like survey. Despite the tiny differences in $Δ_\mathrm{vir}$ between clustering and smooth dark-energy models, owing to the large volumes covered by these surveys, five out of seven clustering dark-energy models can be statistically distinguished from $Λ$CDM. The contribution of dark-energy fluctuation cannot be neglected, especially for the Chevallier-Polarski-Limber and Albrecht-Skordis models, provided the instrumental configurations provide high signal-to-noise ratio. These results are almost independent of the tidal virialization model.

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