Exploring the Geometry, Topology and Morphology of Large Scale Structure using Minkowski Functionals

Modern redshift surveys such as the 2 degree field Galaxy Redshift Survey (2dFGRS) and the Sloan Digital Sky Survey (SDSS) reveal the fully 3 dimensional distribution of a million or so galaxies over a large cosmological volume. Visually galaxies appear to be distributed along sheet-like and/or filamentary superclusters. The CfA Great Wall, Southern Great Wall and the recently discovered SDSS Great Wall are very spectacular superclusters. Clearly theoretical predictions for galaxy clustering must be tested against these rich datasets. This can be achieved by means of the Minkowski Functionals (MFs). A MF-based approach provides an excellent description of superclusters and voids and allows one to quantify the properties of the cosmic web. In this review we give a summary of the progress made in this direction. After reviewing the status of observations and of numerical simulations, we comment upon the nature of bias which serves as a link between theoretical predictions and observations. We also summarise the methods developed for efficient numerical estimation of MFs for cosmological datasets and list several important results obtained using these methods. Specifically, we stress the discriminatory power of MFs and of the derived morphological statistics, the Shapefinders. Shapefinders are an excellent tool with which to study the shapes and sizes of superclusters and voids. We also discuss some of the important effects of scale-dependent bias which are brought out by a MF-based study of the mock catalogues of galaxies. Such effects, we note, should be accounted for before comparing theoretical models with observations.