We prove that if omega is uniformly distributed on [0, 1], then as T -> infinity, t bar right arrow zeta (i omega T + it + 1/2) converges to a nontrivial random generalized function, which in turn is identified as a product of a very well-behaved random smooth function and a random generalized function known as a complex Gaussian multiplicative chaos distribution. This demonstrates a novel rigorous connection between probabilistic number theory and the theory of multiplicative chaos-the latter is known to be connected to various branches of modern probability theory and mathematical physics. We also investigate the statistical behavior of the zeta function on the mesoscopic scale. We prove that if we let delta(T) approach zero slowly enough...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
Funder: Österreichischen Akademie der Wissenschaften; doi: http://dx.doi.org/10.13039/501100001822Ab...
For a centered d-dimensional Gaussian random vector xi = (xi(1),..., xi (d) ) and a homogeneous func...
We consider powers of the absolute value of the characteristic polynomial of Haar distributed random...
For an N×N random unitary matrix U_N, we consider the random field defined by counting the number of...
Gaussian multiplicative chaos was first constructed in Kahane's seminal paper in 1985 in an attempt ...
Abstract A completely elementary and self-contained proof of convergence of Gaussian multiplicative ...
Resolving a conjecture of Helson, Harper recently established that partial sums of random multiplica...
We introduce a new type of convergence in probability theory, which we call "mod-Gaussian convergenc...
Gaussian Multiplicative Chaos is a way to produce a measure on R[superscript d] (or subdomain of R[s...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
We show in this paper that after proper scalings, the characteristic polynomial of a random unitary ...
We prove that when suitably normalized, small enough powers of the absolute value of the characteris...
The chaos expansion of a random variable with uniform distribution is given. This decomposition is a...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
Funder: Österreichischen Akademie der Wissenschaften; doi: http://dx.doi.org/10.13039/501100001822Ab...
For a centered d-dimensional Gaussian random vector xi = (xi(1),..., xi (d) ) and a homogeneous func...
We consider powers of the absolute value of the characteristic polynomial of Haar distributed random...
For an N×N random unitary matrix U_N, we consider the random field defined by counting the number of...
Gaussian multiplicative chaos was first constructed in Kahane's seminal paper in 1985 in an attempt ...
Abstract A completely elementary and self-contained proof of convergence of Gaussian multiplicative ...
Resolving a conjecture of Helson, Harper recently established that partial sums of random multiplica...
We introduce a new type of convergence in probability theory, which we call "mod-Gaussian convergenc...
Gaussian Multiplicative Chaos is a way to produce a measure on R[superscript d] (or subdomain of R[s...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
We show in this paper that after proper scalings, the characteristic polynomial of a random unitary ...
We prove that when suitably normalized, small enough powers of the absolute value of the characteris...
The chaos expansion of a random variable with uniform distribution is given. This decomposition is a...
In this article we prove that suitable positive powers of the absolute value of the characteristic p...
Funder: Österreichischen Akademie der Wissenschaften; doi: http://dx.doi.org/10.13039/501100001822Ab...
For a centered d-dimensional Gaussian random vector xi = (xi(1),..., xi (d) ) and a homogeneous func...