Restricted percolation critical exponents in high dimensions

TL;DR

This paper investigates the behavior of critical percolation clusters in high-dimensional spaces, providing new scaling laws for connection probabilities and cluster sizes in half-spaces and boxes, advancing understanding beyond two-dimensional cases.

Contribution

It establishes the scaling of connection probabilities and two-point functions for critical percolation in high-dimensional half-spaces and boxes, filling gaps in high-dimensional percolation theory.

Findings

Connection probability from 0 to boundary scales as n^{-3} in half-spaces

Two-point function scaling in high-dimensional boxes determined

Cluster size distribution tail behavior characterized

Abstract

Despite great progress in the study of critical percolation on $\mathbb{Z}^d$ for $d$ large, properties of critical clusters in high-dimensional fractional spaces and boxes remain poorly understood, unlike the situation in two dimensions. Closely related models such as critical branching random walk give natural conjectures for the value of the relevant high-dimensional critical exponents; see in particular the conjecture by Kozma-Nachmias that the probability that $0$ and $(n, n, n, \ldots)$ are connected within $[-n,n]^d$ scales as $n^{-2-2d}$. In this paper, we study the properties of critical clusters in high-dimensional half-spaces and boxes. In half-spaces, we show that the probability of an open connection ("arm") from $0$ to the boundary of a sidelength $n$ box scales as $n^{-3}$. We also find the scaling of the half-space two-point function (the probability of an open connection between two vertices) and the tail of the cluster size distribution. In boxes, we obtain the scaling of the two-point function between vertices which are any macroscopic distance away from the boundary.