# Large n limit for the product of two coupled random matrices

## TL;DR

This paper analyzes the asymptotic behavior of squared singular values of the product of two coupled random matrices, revealing their limiting distribution and universality classes through advanced Riemann-Hilbert techniques.

## Contribution

It introduces a new vector equilibrium problem with constraints and derives the limiting distribution and universality results for the squared singular values of coupled matrices.

## Key findings

- Limiting mean distribution described by a vector equilibrium problem
- Explicit spectral curve obtained for the model
- Universality results include Sine, Meijer-G, and Airy kernels

## Abstract

For a pair of coupled rectangular random matrices we consider the squared singular values of their product, which form a determinantal point process. We show that the limiting mean distribution of these squared singular values is described by the second component of the solution to a vector equilibrium problem. This vector equilibrium problem is defined for three measures with an upper constraint on the first measure and an external field on the second measure. We carry out the steepest descent analysis for a 4 $\times$ 4 matrix-valued Riemann-Hilbert problem, which characterizes the correlation kernel and is related to mixed type multiple orthogonal polynomials associated with the modified Bessel functions. A careful study of the vector equilibrium problem, combined with this asymptotic analysis, ultimately leads to the aforementioned convergence result for the limiting mean distribution, an explicit form of the associated spectral curve, as well as local Sine, Meijer-G and Airy universality results for the squared singular values considered.