Evidence of Random Matrix Corrections for the Large Deviations of Selberg's Central Limit Theorem
Abstract
Selberg's central limit theorem states that the values of , where is a uniform random variable on , is distributed like a Gaussian random variable of mean and standard deviation . It was conjectured by Radziwi{\l}{\l} that this breaks down for values of order , where a multiplicative correction would be present at level , . This constant should be equal to the leading asymptotic for the moment of , as first conjectured by Keating and Snaith using random matrix theory. In this paper, we provide numerical and theoretical evidence for this conjecture. We propose that this correction has a significant effect on the distribution of the maximum of in intervals of size , . The precision of the prediction enables the numerical detection of even for low 's of order . A similar correction appears in the large deviations of the Keating-Snaith central limit theorem for the logarithm of the characteristic polynomial of a random unitary matrix, as first proved by F\'eray, M\'eliot and Nikeghbali.
Keywords
Cite
@article{arxiv.2104.07403,
title = {Evidence of Random Matrix Corrections for the Large Deviations of Selberg's Central Limit Theorem},
author = {Eli Amzallag and Louis-Pierre Arguin and Emma Bailey and Kelvin Hui and Rajesh Rao},
journal= {arXiv preprint arXiv:2104.07403},
year = {2021}
}
Comments
18 pages, 5 figures, added a reference to F\'eray, M\'eliot and Nikeghbali where Theorem 1.1 was proved