Stationary random measures : Covariance asymptotics, variance bounds and central limit theorems

Abstract

We consider covariance asymptotics for linear statistics of a general stationary random measure in terms of its truncated pair correlation measure. These asymptotics are particularly of interest in the case of hyperuniform random measures. We give exact infinite series-expansion formulas for the covariance of smooth statistics of random measures involving higher-order integrals of the truncated correlation measures and higher-order derivatives of the test functions and also, equivalently in terms of their Fourier transforms. Exploiting this, we describe possible covariance and variance asymptotics for Sobolev and indicator statistics. In the smooth case, we show that the order of variance asymptotics drops by even powers and give a simple example of a random measure exhibiting such a variance reduction. In the case of indicator statistics of C1-smooth sets, we derive covariance asymptotics at surface-order scale with the limiting constant depending on the intersection of the boundaries of the two sets. We complement this with a lower bound for random measures with a non-trivial atomic part. Restricting to hyperuniform simple point processes, we prove a central limit theorem for Sobolev and Holder continuous statistics of simple point processes satisfying a certain integral identity for higher-order truncated correlation functions.