Quantifying Cosmic Superstructures

The Large Scale Structure (LSS) found in galaxy redshift surveys and in computer simulations of cosmic structure formation shows a very complex network of galaxy clusters, filaments, and sheets around large voids. Here, we introduce a new algorithm, based on a Minimal Spanning Tree, to find basic structural elements of this network and their properties. We demonstrate how the algorithm works using simple test cases and then apply it to haloes from the Millennium Run simulation (Springel et al. 2005). We show that about 70% of the total halo mass is contained in a structure composed of more than 74,000 individual elements, the vast majority of which are filamentary, with lengths of up to 15 Mpc/h preferred. Spatially more extended structures do exist, as do examples of what appear to be sheet-like configurations of matter. What is more, LSS appears to be composed of a fixed set of basic building blocks. The LSS formed by mass selected subsamples of haloes shows a clear correlation between the threshold mass and the mean extent of major branches, with cluster-size haloes forming structures whose branches can extend to almost 200 Mpc/h - the backbone of LSS to which smaller branches consisting of smaller haloes are attached.Comment: accepted for publication in Monthly Notices of the Royal Astronomical Society; 13 pages, with 14 figures and 3 table

Inter-cluster filaments in a Λ\LambdaCDM Universe

The large--scale structure (LSS) in the Universe comprises a complicated filamentary network of matter. We study this network using a high--resolution simulation of structure formation of a $\Lambda$ Cold Dark Matter cosmology. We investigate the distribution of matter between neighbouring large haloes whose masses are comparable to massive clusters of galaxies. We identify a total of 228 filaments between neighbouring clusters. Roughly half of the filaments are either warped or lie off the cluster--cluster axis. We find that straight filaments on the average are shorter than warped ones. More massive clusters are connected to more filaments than less massive ones on average. This finding indicates that the most massive clusters form at the intersections of the filamentary backbone of LSS. For straight filaments, we compute mass profiles. Radial profiles show a fairly well--defined radius, $r_s$, beyond which the profiles follow an $r^{-2}$ power law fairly closely. For the majority of filaments, $r_s$ lies between 1.5 $h^{-1}$ Mpc and 2.0 $h^{-1}$ Mpc. The enclosed overdensity inside $r_s$ varies between a few times up to 25 times mean density, independent of the length of the filaments. Along the filaments' axes, material is not distributed uniformly. Towards the clusters, the density rises, indicating the presence of the cluster infall regions. In addition, we also find some sheet--like connections between clusters. In roughly a fifth of all cluster--cluster connections where we could not identify a filament or sheet, projection effects lead to filamentary structures in the projected mass distribution. (abridged)Comment: 10 pages, 18 figures; submitted to MNRAS; updated: final version, accepted for publicatio

Clustering of Galaxies in a Hierarchical Universe: I. Methods and Results at z=0

We introduce a new technique for following the formation and evolution of galaxies in cosmological N-body simulations. Dissipationless simulations are used to track the formation and merging of dark matter halos as a function of redshift. Simple prescriptions, taken directly from semi-analytic models of galaxy formation, are adopted for cooling, star formation, supernova feedback and the merging of galaxies within the halos. This scheme enables us to study the clustering properties of galaxies and to investigate how selection by type, colour or luminosity influences the results. In this paper, we study properties of the galaxy distribution at z=0. These include luminosity functions, colours, correlation functions, pairwise peculiar velocities, cluster M/L ratios and star formation rates. We focus on two variants of a CDM cosmology: a high- density model with Gamma=0.21 (TCDM) and a low-density model with Omega=0.3 and Lambda=0.7 (LCDM). Both are normalized to reproduce the I-band Tully-Fisher relation near a circular velocity of 220 km/s. Our results depend strongly both on this normalization and on the prescriptions for star formation and feedback. Very different assumptions are required to obtain an acceptable model in the two cases. For TCDM, efficient feedback is required to suppress the growth of galaxies low-mass field halos. Without it, there are too many galaxies and the correlation function turns over below 1 Mpc. For LCDM, feedback must be weak, otherwise too few L* galaxies are produced and the correlation function is too steep. Given the uncertainties in modelling some of the key physical processes, we conclude that it is not yet possible to draw conclusions about the values of cosmological parameters from studies of this kind. Further work on global star formation and feedback effects is required to narrow the range of possibilitiesComment: 43 pages, Latex, 16 figures included, 2 additional GIF format figures, submitted to MNRA

Peculiar Velocities of Galaxy Clusters

We investigate the peculiar velocities predicted for galaxy clusters by theories in the cold dark matter family. A widely used hypothesis identifies rich clusters with high peaks of a suitably smoothed version of the linear density fluctuation field. Their peculiar velocities are then obtained by extrapolating the similarly smoothed linear peculiar velocities at the positions of these peaks. We test these ideas using large high resolution N-body simulations carried out within the Virgo supercomputing consortium. We find that at early times the barycentre of the material which ends up in a rich cluster is generally very close to a high peak of the initial density field. Furthermore the mean peculiar velocity of this material agrees well with the linear value at the peak. The late-time growth of peculiar velocities is, however, systematically underestimated by linear theory. At the time clusters are identified we find their rms peculiar velocity to be about 40% larger than predicted. Nonlinear effects are particularly important in superclusters. These systematics must be borne in mind when using cluster peculiar velocities to estimate the parameter combination $\sigma_8\Omega^{0.6}$.Comment: 8 pages, 4 figures; submitted to MNRA