Swarm-intelligence-based extraction and manifold crawling along the Large-Scale Structure

ABSTRACT 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 d...

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Published in:Monthly notices of the Royal Astronomical Society Vol. 520; no. 3; pp. 4517 - 4539
Main Authors: Awad, Petra, Peletier, Reynier, Canducci, Marco, Smith, Rory, Taghribi, Abolfazl, Mohammadi, Mohammad, Shin, Jihye, Tiňo, Peter, Bunte, Kerstin
Format: Journal Article
Language:English
Published: London Oxford University Press 01.04.2023
Subjects:
Big Data
Celestial bodies
Density
Dimensional analysis
Galactic clusters
Galaxy distribution
Large scale structure of the universe
Machine learning
Probabilistic models
Stars & galaxies
Swarm intelligence
techniques: miscellaneous
methods: data analysis
large-scale structure of Universe
ISSN:0035-8711, 1365-2966, 1365-2966
Online Access:Get full text
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Summary:ABSTRACT 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.
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ISSN:0035-8711
1365-2966
1365-2966
DOI:10.1093/mnras/stad428