Largest eigenvalue and top eigenvector statistics of large Euclidean random matrices

TL;DR

This paper develops a replica-based framework to analytically characterize the largest eigenvalue and top eigenvector of large Euclidean random matrices, revealing their geometric structure and dependence on distribution moments.

Contribution

It introduces a unified method to compute extremal eigenvalues and eigenvectors of Euclidean random matrices, advancing understanding beyond spectral density to extremal spectral properties.

Findings

Explicit expression for the average largest eigenvalue based on distribution moments

Analytical characterization of the top eigenvector's component distribution

Numerical simulations confirm theoretical predictions

Abstract

Euclidean random matrices arise in a wide range of physical systems where interactions are determined by spatial configurations, including disordered media and cooperative phenomena in atomic ensembles. Unlike classical random matrix ensembles, their entries are strongly correlated through the geometry of the underlying random points, making their analytical treatment challenging. While global spectral properties such as the spectral density are relatively well understood, much less is known about extremal eigenvalues and the associated eigenvectors, despite their central role in applications. Here we address the problem of characterising the largest eigenvalue and the corresponding top eigenvector of large Euclidean random matrices, illustrating the formalism on the case of quadratic distance kernel. For vectors in any dimension $d\geq 1$ drawn independently from a common distribution, we show that both quantities can be computed within a unified replica-based framework, leading to a set of $d+2$ self-consistent equations. This approach yields an explicit expression for the average largest eigenvalue, fully determined by low-order moments of the underlying distribution, and an analytical characterisation of the distribution of top eigenvector's components in the large-$N$ limit. We find that the top eigenvector exhibits a non-trivial geometric structure, with components concentrating on a hypersurface determined by the same parameters controlling the largest eigenvalue. We further perform extensive numerical simulations that confirm these predictions. More broadly, our work provides a general framework to access extremal spectral properties of Euclidean random matrices.