Universality of a double scaling limit near singular edge points in random matrix models
T. Claeys, M. Vanlessen

TL;DR
This paper proves the universality of eigenvalue correlation kernels near singular edge points in random matrix models using Riemann-Hilbert analysis and special solutions of the P_I^2 equation, revealing new asymptotic behaviors.
Contribution
It establishes the universality of the double scaling limit kernel near singular edges, connecting it to solutions of the P_I^2 equation and advancing asymptotic analysis techniques.
Findings
Universality of the correlation kernel near singular endpoints.
Connection of the kernel to P_I^2 equation solutions.
Asymptotics of orthogonal polynomial recurrence coefficients.
Abstract
We consider unitary random matrix ensembles Z_{n,s,t}^{-1}e^{-n tr V_{s,t}(M)}dM on the space of Hermitian n x n matrices M, where the confining potential V_{s,t} is such that the limiting mean density of eigenvalues (as n\to\infty and s,t\to 0) vanishes like a power 5/2 at a (singular) endpoint of its support. The main purpose of this paper is to prove universality of the eigenvalue correlation kernel in a double scaling limit. The limiting kernel is built out of functions associated with a special solution of the P_I^2 equation, which is a fourth order analogue of the Painleve I equation. In order to prove our result, we use the well-known connection between the eigenvalue correlation kernel and the Riemann-Hilbert (RH) problem for orthogonal polynomials, together with the Deift/Zhou steepest descent method to analyze the RH problem asymptotically. The key step in the asymptotic…
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