On the precise deviations of the characteristic polynomial of a random matrix

TL;DR

This paper establishes precise moderate and large deviation estimates for the logarithm of the characteristic polynomial of random matrices, extending previous results to a broader class of ensembles and fluctuation ranges.

Contribution

It provides the first equivalent estimates of the probabilities themselves for the characteristic polynomial fluctuations in circular beta ensembles, generalizing prior work.

Findings

Extended the fluctuation range for precise probability estimates.

Provided equivalent probabilities for the characteristic polynomial deviations.

Applied mod-Gaussian convergence techniques to random matrix theory.

Abstract

In this paper, using techniques developed in our earlier works on the theory of mod-Gaussian convergence, we prove precise moderate and large deviation results for the logarithm of the characteristic polynomial of a random unitary matrix. In the case where the unitary matrix is chosen according to the Haar measure, the logarithms of the probabilities of fluctuations of order $A=O(N)$ of the logarithm of the characteristic polynomial have been estimated by Hughes, Keating and O'Connell. In this work we give an equivalent of the probabilities themselves (without the logarithms), and we do so for the more general case of a matrix from the circular $\beta$ ensemble for any parameter $\beta > 0$. In comparison to previous results from F\'eray-M\'eliot-Nikeghbali (2016) and Dal Borgo-Hovhannisyan-Rouault (2019), we considerably extend the range of fluctuations for which precise estimates can be written.