Superclusters and Voids in the Sloan DSS

Abstract

We analyze quasi-2-dimensional slices of the SDSS EDR. Gaussian smoothing with weighting by the inverse of the selection function provides 2D density fields across the full survey depth. Superclusters (SC) are characterized by a percolation algorithm in the large-scale smoothed field. Group candidates are identified with density maxima in the small-scale smoothed field. The group mass function depends on the SC environmental density. We derive the shape-dependent 3-point correlation function and the void size distribution. These are well reproduced by the galaxies identified in high-resolution $\Lambda$CDM simulations.