On estimating the structure factor of a point process, with applications to hyperuniformity

TL;DR

This paper reviews and derives estimators for the structure factor of point processes, introduces a statistically valid hyperuniformity test, and provides a Python toolbox for systematic analysis.

Contribution

It offers the first asymptotically valid statistical test for hyperuniformity and a comprehensive Python toolbox for structure factor estimation.

Findings

Benchmarking of estimators on various examples

Validation of the hyperuniformity test

Comparison of different estimation methods

Abstract

Hyperuniformity is the study of stationary point processes with a sub-Poisson variance in a large window. In other words, counting the points of a hyperuniform point process that fall in a given large region yields a small-variance Monte Carlo estimation of the volume. Hyperuniform point processes have received a lot of attention in statistical physics, both for the investigation of natural organized structures and the synthesis of materials. Unfortunately, rigorously proving that a point process is hyperuniform is usually difficult. A common practice in statistical physics and chemistry is to use a few samples to estimate a spectral measure called the structure factor. Its decay around zero provides a diagnostic of hyperuniformity. Different applied fields use however different estimators, and important algorithmic choices proceed from each field's lore. This paper provides a systematic survey and derivation of known or otherwise natural estimators of the structure factor. We also leverage the consistency of these estimators to contribute the first asymptotically valid statistical test of hyperuniformity. We benchmark all estimators and hyperuniformity diagnostics on a set of examples. In an effort to make investigations of the structure factor and hyperuniformity systematic and reproducible, we further provide the Python toolbox structure_factor, containing all the estimators and tools that we discuss.