Efficient computation of N -point correlation functions in D dimensions

Abstract

We present efficient algorithms for computing the N -point correlation functions (NPCFs) of random fields in arbitrary D -dimensional homogeneous and isotropic spaces. Such statistics appear throughout the physical sciences and provide a natural tool to describe stochastic processes. Typically, algorithms for computing the NPCF components have O ( n N ) complexity (for a dataset containing n particles); their application is thus computationally infeasible unless N is small. By projecting the statistic onto a suitably defined angular basis, we show that the estimators can be written in a separable form, with complexity O ( n 2 ) or O ( n g log   n g ) if evaluated using a Fast Fourier Transform on a grid of size n g . Our decomposition is built upon the D -dimensional hyperspherical harmonics; these form a complete basis on the ( D − 1 ) sphere and are intrinsically related to angular momentum operators. Concatenation of ( N − 1 ) such harmonics gives states of definite combined angular momentum, forming a natural separable basis for the NPCF. As N and D grow, the number of basis components quickly becomes large, providing a practical limitation to this (and all other) approaches: However, the dimensionality is greatly reduced in the presence of symmetries; for example, isotropic correlation functions require only states of zero combined angular momentum. We provide a Julia package implementing our estimators and show how they can be applied to a variety of scenarios within cosmology and fluid dynamics. The efficiency of such estimators will allow higher-order correlators to become a standard tool in the analysis of random fields.