Measuring the Geometry and Topology of Large Scale Structure using SURFGEN: Methodology and Preliminary Results

Abstract

We present a new ansatz which can successfully be used to determine the morphological properties of the supercluster-void network. The ansatz is based on a surface modelling scheme SURFGEN, which generates a triangulated surface from a discrete data set representing (say) the distribution of galaxies in real (or redshift) space. Four Minkowski functionals -- surface area, volume, extrinsic curvature and genus -- describe the geometry and topology of the supercluster-void network. Ratio's of Minkowski functionals -- Shapefinders -- provide us with an excellent diagnostic of three dimensional shapes of clusters, superclusters and voids. Minkowski functionals and Shapefinders are determined for a triangulated iso-density surface using SURFGEN. SURFGEN is tested against both simply and multiply connected eikonal surfaces such as triaxial ellipsoids and tori. Remarkably, the first three Minkowski functionals are computed to better than 1% accuracy while the fourth (genus) is known exactly. SURFGEN also gives excellent results when applied to Gaussian random fields. Our results indicate that the surface modelling scheme SURFGEN is accurate and robust and can successfully be used to quantify the topology and morphology of the supercluster-void network in the universe. We apply SURFGEN to three cosmological models, $\L$CDM, $\T$CDM and SCDM and obtain interesting new results pertaining to the geometry, morphology and topology of large scale structure.