Circular Jacobi Ensembles and deformed Verblunsky coefficients

TL;DR

This paper introduces a matrix model for the circular Jacobi ensemble using deformed Verblunsky coefficients, linking spectral measures, eigenvalue distributions, and asymptotic behaviors in a unified framework.

Contribution

It proposes a new deformation of Verblunsky coefficients that simplifies the analysis of the circular Jacobi ensemble and establishes convergence and large deviation results.

Findings

Eigenvalues follow the circular Jacobi distribution under certain random coefficient assumptions.

Spectral measures converge to a specific probability measure supported on a unit circle arc.

Large deviations for the empirical spectral distribution are established.

Abstract

Using the spectral theory of unitary operators and the theory of orthogonal polynomials on the unit circle, we propose a simple matrix model for the following circular analogue of the Jacobi ensemble: $$c_{\delta,\beta}^{(n)} \prod_{1\leq k<l\leq n}| e^{\ii\theta_k}-e^{\ii\theta_l}|^\beta\prod_{j=1}^{n}(1-e^{-\ii\theta_j})^{\delta} (1-e^{\ii\theta_j})^{\overline{\delta}} $$ with $\Re \delta > -1/2$. If $e$ is a cyclic vector for a unitary $n\times n$ matrix $U$, the spectral measure of the pair $(U,e)$ is well parameterized by its Verblunsky coefficients $(\alpha_0, ..., \alpha_{n-1})$. We introduce here a deformation $(\gamma_0, >..., \gamma_{n-1})$ of these coefficients so that the associated Hessenberg matrix (called GGT) can be decomposed into a product $r(\gamma_0)... r(\gamma_{n-1})$ of elementary reflections parameterized by these coefficients. If $\gamma_0, ..., \gamma_{n-1}$ are independent random variables with some remarkable distributions, then the eigenvalues of the GGT matrix follow the circular Jacobi distribution above. These deformed Verblunsky coefficients also allow to prove that, in the regime $\delta = \delta(n)$ with $\delta(n)/n \to \dd$, the spectral measure and the empirical spectral distribution weakly converge to an explicit nontrivial probability measure supported by an arc of the unit circle. We also prove the large deviations for the empirical spectral distribution.