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arXiv 2310.20184astro-ph.GA
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图论方法识别星系纤维结构形成的临界阈值

Graph Theoretic Approach Identifies Critical Thresholds at which Galaxy Filamentary Structures Form

Sophia-Gisela Strey, Alexander Castronovo, Kailash Elumalai

AI总结:

本研究针对宇宙中星系纤维结构的形成阈值问题,采用图论将其建模为三维欧几里得网络,结合渗流理论按节点价数约简网络,最终识别出星系纤维团对应的长度尺度、密度等物理特征临界阈值。

摘要:

数值模拟与观测表明,星系并非均匀分布。在宇宙学中,宇宙中已知的最大结构是星系纤维,它由星系在引力作用下的层级成团过程形成。这些结构由连接星系团的壁状结构和桥状结构构成。本文中,我们使用图论将这些结构建模为三维空间中的欧几里得网络。借助渗流理论,我们根据节点的价数对宇宙学图进行约简,以揭示内部最稳固的结构形态。通过对网络施加约束,我们进一步找到了长度尺度、密度等物理特征的阈值,在这些阈值下可识别出星系纤维团。

英文摘要:

Numerical simulations and observations show that galaxies are not uniformly distributed. In cosmology, the largest known structures in the universe are galaxy filaments formed from the hierarchical clustering of galaxies due to gravitational forces. These structures consist of walls and bridges that connect clusters. Here, we use graph theory to model the structures as Euclidean networks in three-dimensional space. Using percolation theory, cosmological graphs are reduced based on the valency of nodes to reveal the inner, most robust structural formation. By constraining the network, we then find thresholds for physical features, such as length-scale and density, at which galaxy filament clusters are identified.

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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