SearcharxivSearch

arXiv · 1403.5687

Phase transition in loop percolation

Abstract

We are interested in the clusters formed by a Poisson ensemble of Markovian loops on infinite graphs. This model was introduced and studied in [LeJ12] and [LL12]. It is a model with long range correlations with two parameters $α$ and $κ$. The non-negative parameter $α$ measures the amount of loops, and $κ$ plays the role of killing on vertices penalizing ($κ>0$) or favoring ($κ<0$) appearance of large loops. It was shown in [LL12] that for any fixed $κ$ and large enough $α$, there exists an infinite cluster in the loop percolation on $\mathbb{Z}^d$. In the present article, we show a non-trivial phase transition on the integer lattice $\mathbb{Z}^d$ ($d\geq 3$) for $κ=0$. More precisely, we show that there is no loop percolation for $κ=0$ and $α$ small enough. Interestingly, we observe a critical like behavior on the whole sub-critical domain of $α$, namely, for $κ=0$ and any sub-critical value of $α$, the probability of one-arm event decays at most polynomially. For $d\geq 5$, we prove that there exists a non-trivial threshold for the finiteness of the expected cluster size. For $α$ below this threshold, we calculate, up to a constant factor, the decay of the probability of one-arm event, two point function, and the tail distribution of the cluster size. These rates are comparable with the ones obtained from a single large loop and only depend on the dimension. For $d=3$ or $4$, we give better lower bounds on the decay of the probability of one-arm event, which show importance of small loops for long connections. In addition, we show that the one-arm exponent in dimension $3$ depends on the intensity $α$. [LeJ12] Y. Le Jan, Amas de lacets markoviens, C. R. Math. Acad. Sci. Paris 350 (2012), no.13-14, 643-646. [LL12] Y. Le Jan and S. Lemaire, Markovian loop clusters on graphs, arXiv.org:1211.0300

Explore related subjects

Keep this discovery

BibTeXRIS

Yinshan Chang, Artëm Sapozhnikov. 2014-03-22. Phase transition in loop percolation. https://arxiv.org/abs/1403.5687

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR