Tracy-Widom method for Janossy density and joint distribution of extremal eigenvalues of random matrices

TL;DR

This paper introduces a Tracy-Widom method for expressing Janossy densities of determinantal point processes as Fredholm determinants, enabling analysis of extremal eigenvalues in random matrices without relying on isomonodromic systems.

Contribution

The paper develops a new approach to compute Janossy densities using Tracy-Widom theory, avoiding isomonodromic systems and applying it to eigenvalue distributions in random matrices.

Findings

Expressed Janossy densities as Fredholm determinants of transformed kernels.

Applied the method to Airy and Bessel kernels for eigenvalue distributions.

Demonstrated the approach with explicit calculations for specific cases.

Abstract

The J\'{a}nossy density for a determinantal point process is the probability density that an interval $I$ contains exactly $p$ points except for those at $k$ designated loci. The J\'{a}nossy density associated with an integrable kernel $\mathbf{K}\doteq (\varphi(x)\psi(y)-\psi(x)\varphi(y))/(x-y)$ is shown to be expressed as a Fredholm determinant $\mathrm{Det}(\mathbb{I}-\tilde{\mathbf{K}}|_I)$ of a transformed kernel $\tilde{\mathbf{K}}\doteq (\tilde{\varphi}(x)\tilde{\psi}(y)-\tilde{\psi}(x)\tilde{\varphi}(y))/(x-y)$. We observe that $\tilde{\mathbf{K}}$ satisfies Tracy and Widom's criteria if $\mathbf{K}$ does, because of the structure that the map $(\varphi, \psi)\mapsto (\tilde{\varphi}, \tilde{\psi})$ is a meromorphic $\mathrm{SL}(2,\mathbb{R})$ gauge transformation between covariantly constant sections. This observation enables application of the Tracy--Widom method to J\'{a}nossy densities, expressed in terms of a solution to a system of differential equations in the endpoints of the interval. Our approach does not explicitly refer to isomonodromic systems associated with Painlev\'{e} equations employed in the preceding works. As illustrative examples we compute J\'{a}nossy densities with $k=1, p=0$ for Airy and Bessel kernels, related to the joint distributions of the two largest eigenvalues of random Hermitian matrices and of the two smallest singular values of random complex matrices.