期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 520, 期 3, 页码 4517-4539出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stad428
关键词
methods: data analysis; techniques: miscellaneous; large-scale structure of Universe
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe, known as the Large-Scale Structure or the Cosmic Web, follows an intricate pattern. The 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) toolbox is able to extract structures in the Cosmic Web and create probabilistic models of them. Compared to other methodologies, 1-DREAM is able to split the network into its various environments with comparable results.
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work, we analyse the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of one-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code disperse, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analysing sparse observational data, and finding faint and diffuse manifolds in low-density regions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据