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Behavior of the distance exponent for $\frac{1}{|x-y|^{2d}}$ long-range percolation

Johannes Bäumler

Abstract

We study independent long-range percolation on $\mathbb{Z}^d$ where the vertices $u$ and $v$ are connected with probability asymptotic to $\fracβ{\|u-v\|^{2d}}$ for $\|u-v\|_\infty\geq 2$ and with probability 1 for $\|u-v\|_\infty=1$, where $β\geq 0$ is a parameter. It is proven in [5] that there exists an exponent $θ=θ(d,β) \in \left(0,1\right]$ such that the graph distance between the origin $\mathbf{0}$ and $x \in \mathbb{Z}^d$ scales like $\|x\|^θ$. We prove that this exponent $θ(d,β)$ is continuous and strictly decreasing as a function in $β$. Furthermore, we show that $θ(d,β)=1-β+o(β)$ for small $β$ in dimension $d=1$.

Behavior of the distance exponent for $\frac{1}{|x-y|^{2d}}$ long-range percolation

Abstract

We study independent long-range percolation on where the vertices and are connected with probability asymptotic to for and with probability 1 for , where is a parameter. It is proven in [5] that there exists an exponent such that the graph distance between the origin and scales like . We prove that this exponent is continuous and strictly decreasing as a function in . Furthermore, we show that for small in dimension .
Paper Structure (16 sections, 27 theorems, 313 equations)

This paper contains 16 sections, 27 theorems, 313 equations.

Key Result

Theorem 1.1

The distance exponent $\theta : \mathbb{R}_{\geq 0} \rightarrow \left(0,1\right]$ is strictly monotonically decreasing.

Theorems & Definitions (51)

  • Theorem 1.1
  • Theorem 1.2
  • Theorem 1.3
  • Lemma 2.1: Russo's formula for expectations
  • Lemma 3.1
  • Remark 3.2
  • proof : Proof of Theorem \ref{['theo:smallbeta']}
  • Lemma 4.1
  • Lemma 4.2
  • Lemma 4.3
  • ...and 41 more