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Lace Expansion and Mean-Field Behavior for the Random Connection Model

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arxiv 1908.11356 v4 pith:PXTCP2Y7 submitted 2019-08-29 math.PR

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keywords modelversionconnectionexpansionlacemean-fieldbehaviorbound
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abstract

We study the random connection model driven by a stationary Poisson process. In the first part of the paper, we derive a lace expansion with remainder term in the continuum and bound the coefficients using a new version of the BK inequality. For our main results, we consider three versions of the connection function $\varphi$: a finite-variance version (including the Boolean model), a spread-out version, and a long-range version. For sufficiently large dimension (resp., spread-out parameter and $d>6$), we then prove the convergence of the lace expansion, derive the triangle condition, and establish an infra-red bound. From this, mean-field behavior of the model can be deduced. As an example, we show that the critical exponent $\gamma$ takes its mean-field value $\gamma=1$ and that the percolation function is continuous.

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Cited by 2 Pith papers

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  1. Poisson approximation of large-lifetime cycles

    math.PR 2024-12 conditional novelty 7.0 of 10

    Large-lifetime persistence cycles in Poisson point clouds converge to Poisson point processes, jointly for centers, lifetimes, and deathtimes, in a sparse regime for dimensions d≥2.

  2. Gaussian deconvolution on $\mathbb R^d$ with application to self-repellent Brownian motion

    math.PR 2024-11 accept novelty 6.0 of 10

    A general deconvolution theorem on R^d yields |x|^{-(d-2)} decay, and it is used to prove the critical two-point function of self-repellent Brownian motion is asymptotic to a constant times |x|^{-(d-2)}.

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