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Decay of correlations and zeros for the hard-core model

Han Peters, Josias Reppekus, Guus Regts

Abstract

In a recent paper the last author proved that absence of complex zeros of the partition function of the hard-core model near a parameter $λ>0$ implies a form of correlation decay called strong spacial mixing. In this paper we investigate the reverse implication. We introduce a strengthening of strong spatial mixing that we call very strong spatial mixing (VSSM). Our main result is that if VSSM holds at a parameter $λ>0$ for a family of graphs, this implies that the partition function has no zeros near that parameter for each graph in the family. We also demonstrate that a closely related variant of very strong spatial mixing does not imply zero-freeness. As a consequence of our main result, we moreover obtain that VSSM implies spectral independence. Our proof relies on transforming the problem to the analysis of an induced non-autonomous dynamical system given by Möbius transformations.

Decay of correlations and zeros for the hard-core model

Abstract

In a recent paper the last author proved that absence of complex zeros of the partition function of the hard-core model near a parameter implies a form of correlation decay called strong spacial mixing. In this paper we investigate the reverse implication. We introduce a strengthening of strong spatial mixing that we call very strong spatial mixing (VSSM). Our main result is that if VSSM holds at a parameter for a family of graphs, this implies that the partition function has no zeros near that parameter for each graph in the family. We also demonstrate that a closely related variant of very strong spatial mixing does not imply zero-freeness. As a consequence of our main result, we moreover obtain that VSSM implies spectral independence. Our proof relies on transforming the problem to the analysis of an induced non-autonomous dynamical system given by Möbius transformations.
Paper Structure (13 sections, 25 theorems, 124 equations, 3 figures)

This paper contains 13 sections, 25 theorems, 124 equations, 3 figures.

Key Result

Lemma 3

Let $G$ be a graph with vertex $v$. Then

Figures (3)

  • Figure 1: The components of the tree $T$.
  • Figure 2: A commutative diagram displaying the relation between the $f_n$ and the $g_n$.
  • Figure 3: How ratios propagate upwards in the tree towards a root vertex $v$ in $T_{H}$ (left) and in $P_{1}$ (right). Shown is $B_{\ell}(v)\subseteq H$. $\tau$ is a boundary condition on $S_H(v,\ell)$. In the left picture, $P_n$ examplifies a path that does not intersect the boundary $S_H(v,\ell)$, so $\tau$ has no influence along this path.

Theorems & Definitions (56)

  • Definition 1: The tree of self avoiding walks
  • Remark 2
  • Lemma 3: Weitz Weitz2006CountingIndependentSetsuptotheTreeThreshold
  • Definition 4
  • Remark 5
  • Remark 6
  • Remark 7
  • Theorem 8
  • Definition 9
  • Theorem 10
  • ...and 46 more