Subcritical phase of $d$-dimensional Poisson-Boolean percolation and its vacant set

TL;DR

This paper proves the existence of a sharp phase transition in Poisson-Boolean percolation on -dimensional space, establishing finite expected cluster size in the subcritical regime and a mean-field lower bound in the supercritical regime, without assuming bounded radius distribution.

Contribution

It demonstrates sharp phase transition results for Poisson-Boolean percolation in any dimension under minimal moment conditions, extending previous work to unbounded radius distributions.

Findings

Finite expected cluster size in subcritical regime.

Exponential decay of connection probability with finite exponential moments.

Mean-field lower bound for the probability of infinite cluster in supercritical regime.

Abstract

We prove that the Poisson-Boolean percolation on $\mathbb{R}^d$ undergoes a sharp phase transition in any dimension under the assumption that the radius distribution has a $5d-3$ finite moment (in particular we do not assume that the distribution is bounded). More precisely, we prove that: -In the whole subcritical regime, the expected size of the cluster of the origin is finite, and furthermore we obtain bounds for the origin to be connected to distance $n$: when the radius distribution has a finite exponential moment, the probability decays exponentially fast in $n$, and when the radius distribution has heavy tails, the probability is equivalent to the probability that the origin is covered by a ball going to distance $n$. - In the supercritical regime, it is proved that the probability of the origin being connected to infinity satisfies a mean-field lower bound. The same proof carries on to conclude that the vacant set of Poisson-Boolean percolation on $\mathbb{R}^d$ undergoes a sharp phase transition. This paper belongs to a series of papers using the theory of randomized algorithms to prove sharpness of phase transitions.