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Sensitive bootstrap percolation second term

Ivailo Hartarsky

Abstract

In modified two-neighbour bootstrap percolation in two dimensions each site of $\mathbb Z^2$ is initially independently infected with probability $p$ and on each discrete time step one additionally infects sites with at least two non-opposite infected neighbours. In this note we establish that for this model the second term in the asymptotics of the infection time $τ$ unexpectedly scales differently from the classical two-neighbour model, in which arbitrary two infected neighbours are required. More precisely, we show that for modified bootstrap percolation with high probability as $p\to0$ it holds that \[τ\le \exp\left(\frac{π^2}{6p}-\frac{c\log(1/p)}{\sqrt p}\right)\] for some positive constant $c$, while the classical model is known to lack the logarithmic factor.

Sensitive bootstrap percolation second term

Abstract

In modified two-neighbour bootstrap percolation in two dimensions each site of is initially independently infected with probability and on each discrete time step one additionally infects sites with at least two non-opposite infected neighbours. In this note we establish that for this model the second term in the asymptotics of the infection time unexpectedly scales differently from the classical two-neighbour model, in which arbitrary two infected neighbours are required. More precisely, we show that for modified bootstrap percolation with high probability as it holds that for some positive constant , while the classical model is known to lack the logarithmic factor.
Paper Structure (4 sections, 5 theorems, 19 equations, 1 figure)

This paper contains 4 sections, 5 theorems, 19 equations, 1 figure.

Key Result

theorem 1

For the modified two-neighbour bootstrap percolation model in two dimensions there exists $c>0$ such that

Figures (1)

  • Figure 1: The two growth mechanisms. Shaded rectangles are required to be occupied; the hatched one is not occupied; the black site is infected.

Theorems & Definitions (13)

  • theorem 1
  • proposition 1: Filling probability
  • proof : Proof of Theorem \ref{['th:main']}
  • definition 1: Diagonal growth
  • definition 2: Sideways growth
  • definition 3: Alternating growth
  • lemma 1
  • proof
  • lemma 2: Entropic gain
  • proof
  • ...and 3 more