Random Euclidean coverage from within

TL;DR

This paper studies the distribution and asymptotic behavior of the minimal radius needed to cover a domain with balls centered at random points, providing explicit formulas and laws of large numbers for different dimensions and shapes.

Contribution

It derives the limiting distribution and strong laws of large numbers for the coverage threshold in random Euclidean coverage, including boundary effects and generalizations.

Findings

Explicit limiting distributions for coverage radius in 2D and 3D.

Almost sure convergence of scaled coverage radius to 1.

Results extend to polytopes and non-uniform densities.

Abstract

Let $X_1,X_2, \ldots $ be independent random uniform points in a bounded domain $A \subset \mathbb{R}^d$ with smooth boundary. Define the coverage threshold $R_n$ to be the smallest $r$ such that $A$ is covered by the balls of radius $r$ centred on $X_1,\ldots,X_n$. We obtain the limiting distribution of $R_n$ and also a strong law of large numbers for $R_n$ in the large-$n$ limit. For example, if $A$ has volume 1 and perimeter $|\partial A|$, if $d=3$ then $\Pr[n\pi R_n^3 - \log n - 2 \log (\log n) \leq x]$ converges to $\exp(-2^{-4}\pi^{5/3} |\partial A| e^{-2 x/3})$ and $(n \pi R_n^3)/(\log n) \to 1$ almost surely, and if $d=2$ then $\Pr[n \pi R_n^2 - \log n - \log (\log n) \leq x]$ converges to $\exp(- e^{-x}- |\partial A|\pi^{-1/2} e^{-x/2})$. We give similar results for general $d$, and also for the case where $A$ is a polytope. We also generalize to allow for multiple coverage. The analysis relies on classical results by Hall and by Janson, along with a careful treatment of boundary effects. For the strong laws of large numbers, we can relax the requirement that the underlying density on $A$ be uniform.