Large deviations of the maximal eigenvalue of random matrices

TL;DR

This paper develops a method to compute large deviation functions for the maximum eigenvalue in beta-ensembles of random matrices, extending results to any beta and verifying with numerical checks.

Contribution

It introduces a general approach to calculate the left tail of the maximum eigenvalue distribution for any beta, including the constant term, using loop equations.

Findings

Computed large deviation functions for beta-ensembles.

Extended Tracy-Widom law tail analysis to arbitrary beta.

Validated results with numerical simulations.

Abstract

We present detailed computations of the 'at least finite' terms (three dominant orders) of the free energy in a one-cut matrix model with a hard edge a, in beta-ensembles, with any polynomial potential. beta is a positive number, so not restricted to the standard values beta = 1 (hermitian matrices), beta = 1/2 (symmetric matrices), beta = 2 (quaternionic self-dual matrices). This model allows to study the statistic of the maximum eigenvalue of random matrices. We compute the large deviation function to the left of the expected maximum. We specialize our results to the gaussian beta-ensembles and check them numerically. Our method is based on general results and procedures already developed in the literature to solve the Pastur equations (also called "loop equations"). It allows to compute the left tail of the analog of Tracy-Widom laws for any beta, including the constant term.