The geometry of second-order statistics - biases in common estimators

Abstract

Second-order measures, such as the two-point correlation function, are geometrical quantities describing the clustering properties of a point distribution. In this article well-known estimators for the correlation integral are reviewed and their relation to geometrical estimators for the two-point correlation function is put forward. Simulations illustrate the range of applicability of these estimators. The interpretation of the two-point correlation function as the excess of clustering with respect to Poisson distributed points has led to biases in common estimators. Comparing with the approximately unbiased geometrical estimators, we show how biases enter the estimators introduced by Davis and Peebles, Landy and Szalay, and Hamilton. We give recommendations for the application of the estimators, including details of the numerical implementation. The properties of the estimators of the correlation integral are illustrated in an application to a sample of IRAS galaxies. It is found that, due to the limitations of current galaxy catalogues in number and depth, no reliable determination of the correlation integral on large scales is possible. In the sample of IRAS galaxies considered, several estimators using different finite-size corrections yield different results on scales larger than 20 Mpc/h, while all of them agree on smaller scales.