Pfaffian point process for the Gaussian real generalised eigenvalue problem

TL;DR

This paper derives the joint probability density function and correlation functions for the generalized eigenvalues of Gaussian real matrix pairs, revealing Pfaffian structures and Coulomb gas sum rules, with implications for random matrix theory.

Contribution

It provides explicit Pfaffian formulas and limit theorems for the eigenvalue distributions of Gaussian real matrix pairs in the generalized eigenvalue problem.

Findings

Derived joint eigenvalue density function

Established Pfaffian formulas for correlation functions

Proved limit theorems matching real Gaussian matrix eigenvalues

Abstract

The generalised eigenvalues for a pair of $N\times N$ matrices $(X_1,X_2)$ are defined as the solutions of the equation $\det (X_1-\lambda X_2)=0$, or equivalently, for $X_2$ invertible, as the eigenvalues of $X_2^{-1}X_1$. We consider Gaussian real matrices $X_1,X_2$, for which the generalised eigenvalues have the rotational invariance of the half-sphere, or after a fractional linear transformation, the rotational invariance of the unit disk. In these latter variables we calculate the joint eigenvalue probability density function, the probability $p_{N,k}$ of finding $k$ real eigenvalues, the densities of real and complex eigenvalues (the latter being related to an average over characteristic polynomials), and give an explicit Pfaffian formula for the higher correlation functions $\rho_{(k_1,k_2)}$. A limit theorem for $p_{N,k}$ is proved, and the scaled form of $\rho_{(k_1,k_2)}$ is shown to be identical to the analogous limit for the correlations of the eigenvalues of real Gaussian matrices. We show that these correlations satisfy sum rules characteristic of the underlying two-component Coulomb gas.