Abstract
Numerical simulations and observations show that galaxies are not uniformly distributed in the universe but, rather, they are spread across a filamentary structure. In this large-scale pattern, highly dense regions are linked together by bridges and walls, all of them surrounded by vast, nearly-empty areas. While nodes of the network are widely studied in the literature, simulations indicate that half of the mass budget comes from a more diffuse part of the network, which is made up of filaments. In the context of recent and upcoming large galaxy surveys, it becomes essential that we identify and classify features of the Cosmic Web in an automatic way in order to study their physical properties and the impact of the cosmic environment on galaxies and their evolution. In this work, we propose a new approach for the automatic retrieval of the underlying filamentary structure from a 2D or 3D galaxy distribution using graph theory and the assumption that paths that link galaxies together with the minimum total length highlight the underlying distribution. To obtain a smoothed version of this topological prior, we embedded it in a Gaussian mixtures framework. In addition to a geometrical description of the pattern, a bootstrap-like estimate of these regularised minimum spanning trees allowed us to obtain a map characterising the frequency at which an area of the domain is crossed. Using the distribution of halos derived from numerical simulations, we show that the proposed method is able to recover the filamentary pattern in a 2D or 3D distribution of points with noise and outliers robustness with a few comprehensible parameters.
Highlights
Large galaxy surveys like the Sloan Digital Sky Survey (SDSS, York et al 2000) have confirmed the pattern drawn by matter at very large scales, which was initially addressed in analytical works and the first N-body simulations (e.g. Zel’dovich 1970; Doroshkevich & Shandarin 1978) and which has been exhibited in early observations
We present T-ReX, a graph-based algorithm aimed at an automatic retrieval of the underlying density from a discrete set of points
We show that it can be used to uncover the natural filamentary pattern of the Cosmic Web from a 2D or 3D galaxy distribution
Summary
Large galaxy surveys like the Sloan Digital Sky Survey (SDSS, York et al 2000) have confirmed the pattern drawn by matter at very large scales, which was initially addressed in analytical works and the first N-body simulations (e.g. Zel’dovich 1970; Doroshkevich & Shandarin 1978) and which has been exhibited in early observations (see e.g. Joeveer et al 1978; Einasto et al 1980). The seminal work of Aragon-Calvo et al (2007) allowed Cautun et al (2013) to build Nexus, an algorithm that performs a scale-space representation of the field in which filaments are defined locally through the relative strength between eigenvalues of the Hessian matrix of a smoothed continuous density obtained from the Delaunay Tessellation Field Estimator (Schaap & Weygaert 2000) Another class of methods is based on a statistical representation of stochastic point processes to model the geometry of the filamentary structure. We note that these methods are indirect reconstructions and are not related to our issue of detecting cosmic web elements; Leclercq et al (2016) do use the inferred final density field in a game theory framework to classify structures in the reconstructed density field This wide variety of approaches, all aimed at identifying filaments in a spatial distribution of matter tracers, reveals how this problem can be hard to handle and how great an importance it holds for observational cosmology. ×104 in Libeskind et al (2017), who proposed a unified comparison of the main existing procedures to classify elements of the cosmic web using either dark matter particles or dark matter halos as input
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