Persistence diagrams of random matrices via Morse theory: universality and a new spectral diagnostic

TL;DR

This paper links the persistence diagram of quadratic forms on spheres to eigenvalues of matrices, revealing universality properties in random matrix theory and proposing persistence entropy as a new spectral diagnostic tool.

Contribution

It analytically connects persistence diagrams to eigenvalues, demonstrating universality across RMT ensembles and introducing persistence entropy as a novel spectral diagnostic.

Findings

Persistence diagrams are determined by eigenvalues of symmetric matrices.

Persistence entropy for GOE matrices is explicitly derived as log(8n/π) - 1.

Persistence entropy outperforms traditional level spacing ratios in distinguishing RMT classes.

Abstract

We prove that the persistence diagram of the sublevel set filtration of the quadratic form f(x) = x^T M x restricted to the unit sphere S^{n-1} is analytically determined by the eigenvalues of the symmetric matrix M. By Morse theory, the diagram has exactly n-1 finite bars, with the k-th bar living in homological dimension k-1 and having length equal to the k-th eigenvalue spacing s_k = \lambda_{k+1} - \lambda_k. This identification transfers random matrix theory (RMT) universality to persistence diagram universality: for matrices drawn from the Gaussian Orthogonal Ensemble (GOE), we derive the closed-form persistence entropy PE = log(8n/\pi) - 1, and verify numerically that the coefficient of variation of persistence statistics decays as n^{-0.6}. Different random matrix ensembles (GOE, GUE, Wishart) produce distinct universal persistence diagrams, providing topological fingerprints of RMT universality classes. As a practical consequence, we show that persistence entropy outperforms the standard level spacing ratio \langle r \rangle for discriminating GOE from GUE matrices (AUC 0.978 vs. 0.952 at n = 100, non-overlapping bootstrap 95% CIs), and detects global spectral perturbations in the Rosenzweig-Porter model to which \langle r \rangle is blind. These results establish persistence entropy as a new spectral diagnostic that captures complementary information to existing RMT tools.