论文标题

通过图熵镜头的宇宙网络

The cosmic web through the lens of graph entropy

论文作者

García-Alvarado, María Valentina, Forero-Romero, Jaime E., Li, Xiao-Dong

论文摘要

我们探索图形作为标量的信息理论熵,以量化宇宙网络。我们发现熵值在1.5和3.2位之间。我们认为,该熵可以用作用于量化连续密度场中连通性的标量的离散类似物。在表明熵清楚地区分了聚类和随机点之后,我们使用模拟来衡量调查几何形状,宇宙方差,红移空间扭曲,红移进化,宇宙学参数和空间数密度的影响。宇宙差异显示出最不重要的影响,而调查几何形状,红移空间扭曲,宇宙学参数和红移进化的变化会产生$ 10^{ - 2} $位的阶段的更大变化。对图熵的最大影响来自聚集点的数量密度的变化。随着数量密度的降低,宇宙网络的较不明显,熵最多可以减小0.2位。图形熵易于计算,可以将大型Galaxy红移调查的模拟和观察数据应用于模拟和观察数据。这是一个新的统计量,可以以互补的方式用于其他类型的拓扑或聚类测量。

We explore the information theory entropy of a graph as a scalar to quantify the cosmic web. We find entropy values in the range between 1.5 and 3.2 bits. We argue that this entropy can be used as a discrete analogue of scalars used to quantify the connectivity in continuous density fields. After showing that the entropy clearly distinguishes between clustered and random points, we use simulations to gauge the influence of survey geometry, cosmic variance, redshift space distortions, redshift evolution, cosmological parameters and spatial number density. Cosmic variance shows the least important influence while changes from the survey geometry, redshift space distortions, cosmological parameters and redshift evolution produce larger changes on the order of $10^{-2}$ bits. The largest influence on the graph entropy comes from changes in the number density of clustered points. As the number density decreases, and the cosmic web is less pronounced, the entropy can diminish up to 0.2 bits. The graph entropy is simple to compute and can be applied both to simulations and observational data from large galaxy redshift surveys; it is a new statistic that can be used in a complementary way to other kinds of topological or clustering measurements.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源