From Berry-Esseen to super-exponential

TL;DR

This paper investigates the rate at which traces of powers of Haar-distributed random matrices converge to Gaussian distributions, providing bounds that interpolate between fixed and large power regimes across orthogonal, unitary, and symplectic groups.

Contribution

It derives new bounds on the total variation distance for the convergence of traces of matrix powers to Gaussian limits, extending previous results and covering a broad range of parameters.

Findings

Total variation bounds depend on gamma functions and parameters m, n.

Results interpolate between fixed m and large m regimes.

Sharp asymptotics and lower bounds for m=1 case.

Abstract

For any integer $m<n$, where $m$ can depend on $n$, we study the rate of convergence of $\frac{1}{\sqrt{m}}\mathrm{Tr} \mathbf{U}^m$ to its limiting Gaussian as $n\to\infty$ for orthogonal, unitary and symplectic Haar distributed random matrices $\mathbf{U}$ of size $n$. In the unitary case, we prove that the total variation distance is less than $\Gamma(\lfloor n/m \rfloor+2)^{-1} m^{- \lfloor n/m\rfloor} \lfloor n/m \rfloor^{1/4}\sqrt{\log n}$ times a constant. This result interpolates between the super-exponential bound obtained for fixed $m$ and the $1/n$ bound coming from the Berry-Esseen theorem applicable when $m\ge n$ by a result of Rains. We obtain analogous results for the orthogonal and symplectic groups. In these cases, our total variation upper bound takes the form $\Gamma(2\lfloor n/m\rfloor+1)^{-1/2}m^{-\lfloor n/m\rfloor +1}(\log n)^{1/4}$ times a constant and the result holds provided $n \geq 2m$. For $m=1$, we obtain complementary lower bounds and precise asymptotics for the $L^2$-distances as $n\to\infty$, which show how sharp our results are.