Universal scaling and criticality of extremes in random matrix theory

TL;DR

This paper investigates the behavior of extreme eigenvalues in a random matrix model related to percolation, revealing universal scaling laws and critical phenomena through numerical and analytical methods.

Contribution

It introduces a universal scaling law for extreme eigenvalues in a random matrix model, linking criticality and finite-size effects in complex systems.

Findings

First extreme eigenvalue exhibits Gaussian statistics and critical scaling.

Second extreme eigenvalue follows semicircle distribution with a shifted edge.

Power-law divergences occur at eigenvalue coalescence in the thermodynamic limit.

Abstract

We present a random-matrix realization of a two-dimensional percolation model with the occupation probability $p$. We find that the behavior of the model is governed by the two first extreme eigenvalues. While the second extreme eigenvalue resides on the moving edge of the semicircle bulk distribution with an additional semicircle functionality on $p$, the first extreme exhibits a disjoint isolated Gaussian statistics which is responsible for the emergence of a rich finite-size scaling and criticality. Our extensive numerical simulations along with analytical arguments unravel the power-law divergences due to the coalescence of the first two extreme eigenvalues in the thermodynamic limit. We develop a scaling law that provides a universal framework in terms of a set of scaling exponents uncovering the full finite-size scaling behavior of the extreme eigenvalue's fluctuation. Our study may provide a simple practical approach to capture the criticality in complex systems and their inverse problems with a possible extension to the interacting systems.