Phase separation in random cluster models I: uniform upper bounds on local deviation
Alan Hammond

TL;DR
This paper establishes uniform upper bounds on the local deviation of droplets in the random cluster model, confirming the n^{1/3} scaling with logarithmic corrections, and introduces techniques applicable to stochastic interface models.
Contribution
It provides the first rigorous proof of uniform upper bounds on local deviation in the droplet boundary within the random cluster model, with implications for related stochastic interface models.
Findings
Maximum local roughness scales as n^{1/3} with logarithmic corrections.
Maximum facet length also follows a similar scaling law.
Results suggest Gaussian effects constrained by curvature govern local deviations.
Abstract
This is the first in a series of three papers that addresses the behaviour of the droplet that results, in the percolating phase, from conditioning the Fortuin-Kasteleyn planar random cluster model on the presence of an open dual circuit Gamma_0 encircling the origin and enclosing an area of at least (or exactly) n^2. (By the Fortuin-Kasteleyn representation, the model is a close relative of the droplet formed by conditioning the Potts model on an excess of spins of a given type.) We consider local deviation of the droplet boundary, measured in a radial sense by the maximum local roughness, MLR(Gamma_0), this being the maximum distance from a point in the circuit Gamma_0 to the boundary of the circuit's convex hull; and in a longitudinal sense by what we term maximum facet length, MFL(Gamma_0), namely, the length of the longest line segment of which the boundary of the convex hull is…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Random Matrices and Applications · Theoretical and Computational Physics
