Exact finite-size scaling for the random-matrix representation of bond percolation on square lattice

TL;DR

This paper provides an exact finite-size scaling analysis of a random-matrix model for bond percolation on a square lattice, revealing how eigenvalue behavior signals the percolation transition and establishing universal scaling laws.

Contribution

It introduces a universal finite-size scaling law for the extreme eigenvalues of the matrix model, linking eigenvalue coalescence to the percolation transition in a novel way.

Findings

Percolation transition detected by eigenvalue divergence

Universal scaling law for eigenvalue fluctuations

Relative entropy identifies the scaling framework

Abstract

We report on the exact treatment of a random-matrix representation of bond percolation model on a square lattice in two dimensions with occupation probability $p$. The percolation problem is mapped onto a random complex matrix composed of two random real-valued matrices of elements $+1$ and $-1$ with probability $p$ and $1-p$, respectively. We find that the onset of percolation transition can be detected by the emergence of power-law divergences due to the coalescence of the first two extreme eigenvalues in the thermodynamic limit. We develop a universal finite-size scaling law that fully characterizes the scaling behavior of the extreme eigenvalue's fluctuation in terms of a set of universal scaling exponents and amplitudes. We make use of the relative entropy as an index of the disparity between two distributions of the first and second-largest extreme eigenvalues, to show that its minimum underlies the scaling framework. Our study may provide an inroad for developing new methods and algorithms with diverse applications in machine learning, complex systems, and statistical physics.