论文标题
扩展宇宙结构的空间相关性
Spatial correlations of extended cosmological structures
论文作者
论文摘要
宇宙中的大规模结构的研究,例如上层建筑或宇宙空隙,已被广泛用于通过统计分析来表征宇宙网络的性质。另一方面,大规模示踪剂(例如星系或光环)的2分相关函数提供了可靠的统计措施。但是,此函数适用于点状对象的空间分布,因此不适合远离球形对称性的扩展大型结构。在这里,我们提出了基于标准相关函数形式主义的分析,该分析可以应用于显示任意形状的扩展对象。遵循这种方法,我们计算具有与相对于随机位置中相同结构的分布相对应的实现的宇宙结构的一部分的概率过剩$ξ$。为此,我们确定在MPDL2 Multidark模拟上的半射线星系中定义为将未来病毒化结构(FVS)定义的上层建筑。我们还确定了宇宙空隙,以共同研究其相对于上层建筑的相对分布。我们的分析表明,$ξ$从模拟的子集的分析中提出了大规模分布的有用特征。即使上层结构属性可能在子集中表现出可忽略的变化,$ξ$也对统计上的子盒具有敏感性,该子盒在较大尺度上与平均值不同。因此,我们的方法可以应用于对未来调查的分析,以提供适合区分不同理论场景的大规模结构的特征。
Studies of large-scale structures in the Universe, such as superstructures or cosmic voids, have been widely used to characterize the properties of the cosmic web through statistical analyses. On the other hand, the 2-point correlation function of large-scale tracers such as galaxies or halos provides a reliable statistical measure. However, this function applies to the spatial distribution of point-like objects, and therefore it is not appropriate for extended large structures which strongly depart from spherical symmetry. Here we present an analysis based on the standard correlation function formalism that can be applied to extended objects exhibiting arbitrary shapes. Following this approach, we compute the probability excess $Ξ$ of having spheres sharing parts of cosmic structures with respect to a realization corresponding to a distribution of the same structures in random positions. For this aim, we identify superstructures defined as Future Virialized Structures (FVSs) in semi-anaytic galaxies on the MPDL2 MultiDark simulation. We have also identified cosmic voids to provide a joint study of their relative distribution with respect to the superstructures. Our analysis suggests that $Ξ$ provides useful characterizations of the large scale distribution, as suggested from an analysis of sub-sets of the simulation. Even when superstructure properties may exhibit negligible variations across the sub-sets, $Ξ$ has the sensitivity to statistically distinguish sub-boxes that departs from the mean at larger scales. Thus, our methods can be applied in analysis of future surveys to provide characterizations of large-scale structure suitable to distinguish different theoretical scenarios.