Cumulants asymptotics for the zeros counting measure of real Gaussian processes

TL;DR

This paper derives precise asymptotics for cumulants of zero-counting measures in real Gaussian processes, establishing a central limit theorem and refining previous methods for a broad class of processes.

Contribution

It provides a new, more general approach to asymptotics of cumulants for zeros of Gaussian processes, weakening previous assumptions and confirming conjectures for several important classes.

Findings

Higher-order cumulants vanish asymptotically for regular, square-integrable covariance functions.

Number of zeros satisfies a central limit theorem under broad conditions.

Results apply to Gaussian trigonometric and orthogonal polynomials, and sinc kernel processes.

Abstract

We compute the exact asymptotics for the cumulants of linear statistics associated with the zeros counting measure of a large class of real Gaussian processes. Precisely, we show that if the underlying covariance function is regular and square integrable, the cumulants of order higher than two of these statistics asymptotically vanish. This result implies in particular that the number of zeros of such processes satisfies a central limit theorem. Our methods refines the recent approach by T. Letendre and M. Ancona and allows us to prove a stronger quantitative asymptotics, under weaker hypotheses on the underlying process. The proof exploits in particular the elegant interplay between the combinatorial structures of cumulants and factorial moments in order to simplify the determination of the asymptotics of nodal observables. The class of processes addressed by our main theorem englobes as motivating examples random Gaussian trigonometric polynomials, random orthogonal polynomials and the universal Gaussian process with sinc kernel on the real line, for which the asymptotics of higher moments of the number of zeros were so far only conjectured.