Shape statistics of Sloan Digital Sky Survey superclusters

We study the supercluster shape properties of the recently compiled Sloan Digital Sky Survey (SDSS) cluster catalogue using an approach based on differential geometry. We detect superclusters by applying the percolation algorithm to observed cluster populations, extended out to z(max) less than or equal to 0.23 in order to avoid selection biases. We utilize a set of shapefinders in order to study the morphological features of superclusters with greater than or equal to8 cluster members and find that filamentary morphology is the dominant supercluster shape feature, in agreement with previous studies.