Tracing the cosmic web

Abstract

The cosmic web is one of the most striking features of the distribution of galaxies and dark matter on the largest scales in the Universe. It is composed of dense regions packed full of galaxies, long filamentary bridges, flattened sheets and vast low density voids. The study of the cosmic web has focused primarily on the identification of such features, and on understanding the environmental effects on galaxy formation and halo assembly. As such, a variety of different methods have been devised to classify the cosmic web -depending on the data at hand, be it numerical simulations, large sky surveys or other. In this paper we bring twelve of these methods together and apply them to the same data set in order to understand how they compare. In general these cosmic web classifiers have been designed with different cosmological goals in mind, and to study different questions. Therefore one would not a priori expect agreement between different techniques however, many of these methods do converge on the identification of specific features. In this paper we study the agreements and disparities of the different methods. For example, each method finds that knots inhabit higher density regions than filaments, etc. and that voids have the lowest densities. For a given web environment, we find substantial overlap in the density range assigned by each web classification scheme. We also compare classifications on a halo-by-halo basis; for example, we find that 9 of 12 methods classify around a third of group-mass haloes (i.e. M halo ∼ 10 13.5 h −1 M ⊙ ) as being in filaments. Lastly, so that any future cosmic web classification scheme can be compared to the 12 methods used here, we have made all the data used in this paper public.

Revealing the Dark Threads of the Cosmic Web

“…The principal challenge is identifying and quantifying the large-scale structure of the Cosmic Web, particularly in regions where the most diffuse gas is purported to exist. Several groups have developed automatic methods to identify large scale structures and/or derive the underlying density fields (e.g., Aragón-Calvo et al 2007;Cautun et al 2013;Sousbie et al 2011;Tempel et al 2014;Chen et al 2016;Libeskind et al 2018). The difficulty of the problem for all of these methods is compounded by the lack of a 'ground truth' solution for large-scale structures in the observed Universe.…”

Identification of filamentary structures in the environment of superclusters of galaxies in the Local Universe

“…These structures, 'tendrils', have a lower density than the SDSS filaments and appear to be morphologically distinct, they are more isolated and span shorter distances. A comprehensive review and comparative analysis of these algorithms can be found in Libeskind et al (2018).…”

T-ReX: a graph-based filament detection method

“…Finally, to compare our results with other methods, we also apply T-ReX on FoF halos extracted from a 200Mpc/h box of a Gadget-2 N-body simulation with 512 3 particles (Springel et al 2005). This particular simulation 2 is the one used 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.…”

“…With no Article number, page 1 of 15 arXiv:1912.00732v1 [astro-ph.CO] 2 Dec 2019 A&A proofs: manuscript no. aanda aim to be exhaustive (see Libeskind et al 2017, for a detailed review about existing methods to classify cosmic web elements):…”