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The nature of level set percolation in the two-dimension Gaussian Free Field has been an elusive question. Using a loop-model mapping, we show that there is a nontrivial percolation transition, and characterize the critical point. In particular, the correlation length diverges exponentially, and the critical clusters are logarithmic fractals, whose area scales with the linear size as $A sim L^2 / sqrt{ln L}$. The two-point connectivity also decays as the log of the distance. We corroborate our theory by numerical simulations. Possible CFT interpretations are discussed.
Recent studies introduced biased (degree-dependent) edge percolation as a model for failures in real-life systems. In this work, such process is applied to networks consisting of two types of nodes with edges running only between nodes of unlike type
Biased (degree-dependent) percolation was recently shown to provide new strategies for turning robust networks fragile and vice versa. Here we present more detailed results for biased edge percolation on scale-free networks. We assume a network in wh
We consider the zero-average Gaussian free field on a certain class of finite $d$-regular graphs for fixed $dgeq 3$. This class includes $d$-regular expanders of large girth and typical realisations of random $d$-regular graphs. We show that the leve
We study level-set percolation of the Gaussian free field on the infinite $d$-regular tree for fixed $dgeq 3$. Denoting by $h_star$ the critical value, we obtain the following results: for $h>h_star$ we derive estimates on conditional exponential mom
We study the hopping transport of a quantum particle through finite, randomly diluted percolation clusters in two dimensions. We investigate how the transmission coefficient T behaves as a function of the energy E of the particle, the occupation conc