The density distribution arising at the nonlinear stage of gravitational instability is similar to intermittency phenomena in acoustic turbulence. Initially small-amplitude density fluctuations of Gaussian type transform into thin dense pancakes, filaments, and compact clumps of matter. It is perhaps surprising that the motion of self-gravitating matter in the expanding universe is like that of noninteracting matter moving by inertia. A similar process is the distribution of light reflected or refracted from rippled water. The similarity of gravitational instability to acoustic turbulence is highlighted by the fact that late nonlinear stages of density perturbation growth can be described by the Burgers equation, which is well known in the theory of turbulence. The phenomena discussed in this article are closely related to the problem of the formation of large-scale structure of the universe, which is also discussed.

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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.

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Understanding the structure of the matter distribution in the Universe due to the action of the gravitational instability -the cosmic web -is complicated by lack of direct analytic access to the nonlinear domain of structure formation. Here, we suggest and apply a novel tessellation method designed for cold dark matter (CDM) N-body cosmological simulations. The method is based on the fact that the initial CDM state can be described by a 3-D manifold (in a 6-D phase space) that remains continuous under evolution. Our technique uses the full phase space information and has no free parameters; it can be used to compute multi-stream and density fields, the main focus of this paper. Using a large-box ΛCDM simulation we carry out a variety of initial analyses with the technique. These include studying the correlation between multi-streaming and density, the identification of structures such as Zel'dovich pancakes and voids, and statistical measurements of quantities such as the volume fraction as a function of the number of streams -where we find a remarkable scaling relation. Cosmological implications are briefly discussed.PACS numbers:

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We construct a set of shapefinders used to determine the shapes of compact surfaces (isodensity surfaces in galaxy surveys or N-body simulations) without fitting them to ellipsoidal configurations, as has been done earlier.The new indicators, based on the Minkowski functionals, arise from simple, geometrical considerations and are derived from the fundamental properties of a surface such as its volume, surface area, integrated mean curvature, and connectivity characterized by the genus. These "shapefinders" could be used to diagnose the presence of filaments, pancakes, and ribbons in large-scale structure. Their lower dimensional generalization may be useful for the study of two-dimensional distributions such as temperature maps of the cosmic microwave background.