1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments

Abstract

Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied system and hold essential information on its history and future evolution. However, the recovery of such structures is often complicated by the presence of a large amount of background and transverse noise in the observation space. While the former is generally detrimental to the analysis, the latter can be attributed to measurement errors and it can hold essential information about the structure. To further complicate the scenario, 1D manifolds (filaments) are generally non-linear and their geometry difficult to extract and model. In order to study hidden manifolds within the dataset, particular care has to be devoted to background noise removal and transverse noise modelling, while still maintaining accuracy in the recovery of their geometrical structure. We propose 1-DREAM: a toolbox composed of five main Machine Learning methodologies whose aim is to facilitate manifold extraction in such cases. Each methodology has been designed to address issues when dealing with complicated low-dimensional structures convoluted with noise and it has been extensively tested in previously published works. In this work all methodologies are presented in detail, joint within a cohesive framework and demonstrated for three interesting astronomical cases: a simulated jellyfish galaxy, a filament extracted from a simulated cosmic web and the stellar stream of Omega-Centauri as observed with the GAIA DR2. Two newly developed visualization techniques are also proposed, that take full advantage of the results obtained with 1-DREAM. The code is made publicly available to benefit the community. The controlled experiments on a purposefully built data set prove the accuracy of the pipeline in recovering the hidden structures.

References in corpus (19)

• Smoothed Particle Hydrodynamics and Magnetohydrodynamics
• Properties of Dark Matter Haloes in Clusters, Filaments, Sheets and Voids
• The Evolution of Dark Matter Halo Properties in Clusters, Filaments, Sheets and Voids
• The Streams of the Gaping Abyss: A population of entangled stellar streams surrounding the Inner Galaxy
• The darkness that shaped the void: dark energy and cosmic voids
• Angular momentum-Large-scale structure alignments in LCDM models and the SDSS
• The SAMI Galaxy Survey: First detection of a transition in spin orientation with respect to cosmic filaments in the stellar kinematics of galaxies
• Void Ellipticity Distribution as a Probe of Cosmology
• Alignments of Voids in the Cosmic Web
• Stripped elliptical galaxies as probes of ICM physics: I. Tails, wakes, and flow patterns in and around stripped ellipticals
• Stellar streams as gravitational experiments II. Asymmetric tails of globular cluster streams
• The Effect of Ram-Pressure Stripping on Dwarf Galaxies
• The Imprint of Cosmic Web Quenching on Central Galaxies
• On the observability of coupled dark energy with cosmic voids
• Ram pressure stripping in a viscous intracluster medium
• A tale of two tails: insights from simulations into the formation of the peculiar dwarf galaxy NGC 1427A
• Manifold Denoising by Nonlinear Robust Principal Component Analysis
• LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
• Manifold Learning via Manifold Deflation

Cited by in corpus (6)

• Radio continuum tails in ram pressure-stripped spiral galaxies: experimenting with a semi-empirical model in Abell 2255
• Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure
• The large-scale structure around the Fornax-Eridanus Complex
• The SRG/eROSITA all-sky survey. X-ray emission from the warm-hot phase gas in long cosmic filaments
• The spatial distribution of dwarf and giant galaxies in and around Virgo cluster
• The influence of the Cosmic Web on the properties of dwarf galaxies in the Fornax-Eridanus Supercluster