论文标题
暗物质光环的潮汐病毒和聚类的暗能量
Tidal virialization of dark matter haloes with clustering dark energy
论文作者
论文摘要
我们通过考虑将黑暗能源模型的扩展球形塌陷模型中的病毒化过程（即考虑到深色能源波动的延伸），扩展了对Pace等人，JCAP，2019，060的分析。与标准方法的不同，此处的病毒化是通过正确建模与潮汐相互作用引起的剪切和旋转造成的球形偏差来自然实现的。我们研究了七个聚类的动态暗能量模型中病毒过度密度$δ__\ mathrm {vir} $的时间演变，并将结果与$λ$ CDM模型以及相应的平滑暗能模型进行比较。考虑到所有适当的校正，我们推断出Rubin观测值LSST和类似Euclid的弱透镜调查的收敛峰，Sunyaev-Zel'Dovich峰的Simon观测值类似CMB调查以及类似Erosita的X射线峰的Sunyaev-Zel'Dovich峰。尽管$δ_\ mathrm {vir} $之间的差异很小，因此由于这些调查所涵盖的大量批量，七分之五可以在统计上与$λ$ cdm区分开来，但七个聚类的深色能源模型中有五分之五。黑暗能量波动的贡献不能被忽略，尤其是对于Chevallier-Polarski-Limber和Albrecht-Skordis模型，只要仪器构型提供了高信噪比，就可以忽略。这些结果几乎与潮汐病毒模型无关。
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.
