Topology and geometry of Gaussian random fields II: on critical points, excursion sets, and persistent homology

TL;DR

This paper explores the topological and geometric properties of 3D Gaussian random fields using persistent homology, revealing how topological features relate to the power spectrum and providing detailed insights beyond traditional measures.

Contribution

It introduces a topological data analysis approach to characterize Gaussian fields, highlighting the dependence of topological features on the power spectrum and offering new statistical tools.

Findings

Intensity maps reveal detailed power distribution information.

White noise spectrum distinguishes models with different spectral indices.

Topological characteristics depend on the power spectrum, unlike geometric measures.

Abstract

This paper is second in the series, following Pranav et al. (2019), focused on the characterization of geometric and topological properties of 3D Gaussian random fields. We focus on the formalism of persistent homology, the mainstay of Topological Data Analysis (TDA), in the context of excursion set formalism. We also focus on the structure of critical points of stochastic fields, and their relationship with formation and evolution of structures in the universe. The topological background is accompanied by an investigation of Gaussian field simulations based on the LCDM spectrum, as well as power-law spectra with varying spectral indices. We present the statistical properties in terms of the intensity and difference maps constructed from the persistence diagrams, as well as their distribution functions. We demonstrate that the intensity maps encapsulate information about the distribution of power across the hierarchies of structures in more detailed than the Betti numbers or the Euler characteristic. In particular, the white noise ($n = 0$) case with flat spectrum stands out as the divide between models with positive and negative spectral index. It has the highest proportion of low significance features. This level of information is not available from the geometric Minkowski functionals or the topological Euler characteristic, or even the Betti numbers, and demonstrates the usefulness of hierarchical topological methods. Another important result is the observation that topological characteristics of Gaussian fields depend on the power spectrum, as opposed to the geometric measures that are insensitive to the power spectrum characteristics.