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Phase transition for the late points of random walk

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arxiv 2309.03192 v1 pith:D3BDNFUD submitted 2023-09-06 math.PR math-phmath.MP

classification math.PRmath-phmath.MP
keywords alphamathcalpointsepsilonrandomsetstimecoupling
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abstract

Let $X$ be a random walk on the torus of side length $N$ in dimension $d\geq 3$ with uniform starting point, and $t_{\text{cov}}$ be the expected value of its cover time, which is the first time that $X$ has visited every vertex of the torus at least once. For $\alpha > 0$, the set $\mathcal{L}^{\alpha}$ of $\alpha$-late points consists of those points not visited by $X$ at time $\alpha t_{\text{cov}}$. We prove the existence of a value $\alpha_* \in (\frac12,1)$ across which $\mathcal{L}^{\alpha}$ trivialises as follows: for all $\alpha > \alpha_*$ and $\epsilon\geq N^{-c}$ there exists a coupling of $\mathcal{L}^\alpha$ and two occupation sets $\mathcal{B}^{\alpha_\pm}$ of i.i.d. Bernoulli fields having the same density as $\mathcal{L}^{\alpha\pm \epsilon}$, which is asymptotic to $N^{-(\alpha\pm\epsilon)d}$, with the property that the inclusion $ \mathcal{B}^{\alpha_+} \subseteq \mathcal{L}^{\alpha} \subseteq \mathcal{B}^{\alpha_-}$ holds with high probability as $N \to \infty$. On the contrary, when $\alpha \leq \alpha_*$ there is no such coupling. Corresponding results also hold for the vacant set of random interlacements at high intensities. The transition at $\alpha_*$ corresponds to the (dis-)appearance of `double-points' (i.e. neighboring pairs of points) in $\mathcal{L}^\alpha$. We further describe the law of $\mathcal{L}^{\alpha}$ for $\alpha>\frac12$ by adding independent patterns to $\mathcal{B}^{\alpha_{\pm}}$. In dimensions $d \geq 4$ these are exactly all two-point sets. When $d=3$ one must also include all connected three-point sets, but no other.

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  1. Large Deviations of Cover Time of Tori in Dimensions $d\geq 3$

    math.PR 2024-11 conditional novelty 7.0 of 10

    For d ≥ 3 and γ > (d+2)/(2d), the probability that the torus cover time is at most γ times its mean is exp(-(1+o(1)) N^{d(1-γ)}).

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