Statistical properties of quadrangular surfaces

Abstract

We investigate the statistical properties of random quadrangular surfaces generated by different randomization procedures: the Gruzberg-Kl\"umper-Nuding-Sedrakyan (GKNS) construction, and two newly introduced generalizations of dynamical triangulations (DT), dynamical (DQ) and general quadrangulations (GQ). We formulate these surfaces within a unified graph-theoretic framework and establish the relationships between the elementary operations defining the different ensembles. For GKNS surfaces, we demonstrate that the construction is equivalent to two mutually constrained percolation processes and determine the associated critical point and critical exponents, revealing deviations from ordinary percolation. For DQ and GQ, we analyze the underlying Markov chains and determine the scaling of mixing and relaxation times. We further analyze all three ensembles through their degree distributions, degree correlations, distance statistics, and Hausdorff dimensions. While DQ exhibits exponentially decaying degree distributions and geometric properties similar to DT, GKNS and GQ display broad, scale-free degree distributions. Moreover, GKNS surfaces possess an asymptotic Hausdorff dimension $\Delta_H\approx 2$, whereas DQ and GQ approach $\Delta_H\approx 4$, similarly to DT. This indicates that DQ is compatible with the universal behavior of DT, while GKNS and GQ define distinct classes of random geometry, implying a different underlying measure in the space of random surfaces and a possible geometric framework for a new class of noncritical string theories.