Superlarge-scale structure inN-body simulations

Abstract

The simulated matter distribution on large scales is studied using core‐sampling, cluster analysis, inertia tensor analysis and minimal spanning tree techniques. Seven simulations in large boxes for five cosmological models with COBE‐normalized CDM‐like power spectra are studied. A wall‐like superlarge‐scale structure with parameters similar to the observed one is found for the OCDM and ΛCDM models with Οmh = 0.2−0.3. In these simulations, the rich structure elements with a typical value for the largest extension of ∼(30 − 50) h−1 Mpc incorporate ∼40 per cent of matter with overdensity of about 10 above the mean. These rich elements are formed by the anisotropic non‐linear compression of sheets with an original size of ∼(15−25) h−1 Mpc. They surround low‐density regions with a typical diameter ∼(50−70) h−1 Mpc. The statistical characteristics of these structures are found to be approximately consistent with observations and theoretical expectations. The cosmological models with higher matter density Ωm=1 in CDM with Harrison–Zeldovich or tilted power spectra cannot reproduce the characteristics of the observed galaxy distribution because of the very strong disruption of the rich structure elements. Another model with a broken scale‐invariant initial power spectrum (BCDM) does not show enough matter concentration in the rich structure elements.

Detection of 20–30 h$\mathsf{^{-1}}$ Mpc-scale galaxy structures embedded in 100 h$\mathsf{^{-1}}$ Mpc-scale structures of quasars and MgII absorbers at $\textit{z}\simeq\mathsf{0.8}$ and $\textit{z} \simeq\mathsf{1.2}$

“…As clusters and filaments are apparent at z ∼ 1, the sheet-like structures which form before these, must also be substantially in place. This is confirmed by simulations, in which Doroshkevich et al (1999) find that for the present day wall-like structures where the overdensities of matter are a factor 5-10 over the mean, half the mass was already in structures with the same level of overdensities at z ∼ 1. This also can be considered through the simple argument that the characteristic distance scales of large-scale structures (50-100 h −1 Mpc) are much greater than the distances galaxies are likely to move over the time-scales involved, (a galaxy with a peculiar velocity of 100 h −1 km s −1 moves 1 h −1 Mpc over 10 Gyr).…”

“…The Hubble Volume numerical simulations of Evrard et al (2002) find a large cluster at z = 1.04 in a ΛCDM model which has a mass twice that of Coma, making it the largest cluster in their positive octant (PO) survey which covered an eighth of the sky to z = 1.46. In numerical simulations Doroshkevich et al (1999) find that sheet-like large-scale structures with typical maximal extensions of ∼30-50 h −1 Mpc incorporate ∼40% of matter at z = 0, of which ∼60% is in place by z ∼ 1.…”

“…Numerical simulations have allowed the evolution of large-scale structure from the initial Gaussian perturbations to be followed up to the present for a variety of CDM models, successfully reproducing the main properties of the observed large-scale galaxy distribution (e.g. Doroshkevich et al 1999). The most stringent constraints on cosmological models should be provided by direct observation of this evolution through measuring the properties of large-scale structures at the highest possible redshifts; a task beyond the scope of the current large galaxy redshift surveys which reach only z ∼ 0.2-0.3.…”

Search for clustering of background objects near distant radio galaxies using the MST method

“…The MST has become a tool of choice for identification and investigation of connected structures not only in the analysis of the large-scale structure [18,27,28] but also in other astrophysical studies [29][30][31], e.g., in the phase analysis of the CMB distribution [32,33]. In the MST method each distribution of points in space is associated with a special graph referred to as the "minimal spanning tree."…”

“…To construct an MST, we use the tree program described in [27]. To test the performance of the algorithm, we applied it on simulated data: two normal distributions with different centers and dispersions in the Cartesian plane XY with randomly distributed points superimposed (Fig.…”