Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure

TL;DR

This paper introduces 1-DREAM, a machine learning framework for extracting and modeling the large-scale cosmic web structures from cosmological simulation data, demonstrating its robustness and effectiveness compared to existing methods.

Contribution

The paper presents a novel machine learning toolbox, 1-DREAM, capable of extracting and modeling cosmic web structures across different densities, with robustness to sample size variations.

Findings

1-DREAM effectively extracts structures across density ranges.

It produces probabilistic models of cosmic filaments.

It compares favorably with state-of-the-art methods like DisPerSE.

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 analyze 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 1-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 analyzing sparse observational data, and finding faint and diffuse manifolds in low density regions.