Smallest gaps between zeros of stationary Gaussian processes

In this paper, we study the smallest gaps between successive zeros of nondegenerate smooth stationary centered Gaussian processes on the real line with the assumption that the covariance kernel $\kappa(x)$ and its derivatives decay to 0 as $|x|\to\infty$.

We prove that, after rescaling, the smallest gaps converge to a Poisson point process with a specific rate.

Moreover, the positions where these smallest gaps occur tend to a uniform distribution.

Consequently, we can derive the limiting density for the $k$-th smallest gap.