Average characteristic polynomials in the two-matrix model

TL;DR

This paper derives determinantal formulas for averages of characteristic polynomial products and ratios in the two-matrix model, generalizing known results and providing new proofs and generating functions.

Contribution

It introduces explicit determinantal expressions for characteristic polynomial averages in the two-matrix model, extending previous single-matrix results and connecting to biorthogonal polynomials and kernels.

Findings

Determinantal formulas for polynomial averages derived

Generalization of one-matrix model results to two-matrix case

New proof of Eynard-Mehta theorem provided

Abstract

The two-matrix model is defined on pairs of Hermitian matrices $(M_1,M_2)$ of size $n\times n$ by the probability measure $$\frac{1}{Z_n} \exp\left(\textrm{Tr} (-V(M_1)-W(M_2)+\tau M_1M_2)\right)\ dM_1\ dM_2, $$ where $V$ and $W$ are given potential functions and $\tau\in\er$. We study averages of products and ratios of characteristic polynomials in the two-matrix model, where both matrices $M_1$ and $M_2$ may appear in a combined way in both numerator and denominator. We obtain determinantal expressions for such averages. The determinants are constructed from several building blocks: the biorthogonal polynomials $p_n(x)$ and $q_n(y)$ associated to the two-matrix model; certain transformed functions $\P_n(w)$ and $\Q_n(v)$; and finally Cauchy-type transforms of the four Eynard-Mehta kernels $K_{1,1}$, $K_{1,2}$, $K_{2,1}$ and $K_{2,2}$. In this way we generalize known results for the $1$-matrix model. Our results also imply a new proof of the Eynard-Mehta theorem for correlation functions in the two-matrix model, and they lead to a generating function for averages of products of traces.