1996
DOI: 10.1016/0550-3213(95)00664-8
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A real-space renormalization group for random surfaces

Abstract: We propose a new real-space renormalization group transformation for dynamical triangulations. It is shown to preserve geometrical exponents such as the string susceptibility and Hausdorff dimension. We furthermore show evidence for a fixed point structure both in pure gravity and gravity coupled to a critical Ising system. In the latter case we are able to extract estimates for the gravitationally dressed exponents which agree to within 2 − 3% of the KPZ formula.

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Cited by 21 publications
(24 citation statements)
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“…In fact there is another equally valid reason to take them into account. In the dynamical lattice approach of 2-D gravity, there has been several (mostly numerical) attempts to perform real space renormalization [14,24,25,[46][47][48][49][50]. This amounts to replace a block of neighboring cells of the random lattice by a single, larger cell.…”
Section: Discussionmentioning
confidence: 99%
“…In fact there is another equally valid reason to take them into account. In the dynamical lattice approach of 2-D gravity, there has been several (mostly numerical) attempts to perform real space renormalization [14,24,25,[46][47][48][49][50]. This amounts to replace a block of neighboring cells of the random lattice by a single, larger cell.…”
Section: Discussionmentioning
confidence: 99%
“…My motivation is two-fold. First, the coarse graining procedures developed by Johnston, Kownacki, and Krzywicki [30] and by Catterall, Renken, and Thorleifsson [35,36,39] for Euclidean dynamical triangulations and even the coarse graining procedure developed by Henson [27] for causal dynamical triangulations are in fact not well-suited to causal dynamical triangulations: the triangulation obtained after just a single iteration of the procedure is no longer necessarily a causal triangulation. (Indeed, the output of Henson's coarse graining procedure might not even be a simplicial manifold.)…”
Section: Construction Of the Renormalization Group Flowsmentioning
confidence: 99%
“…Additional evidence for this claim is obtained by applying a recently proposed Monte Carlo renormalization group method for blocking dynamical triangulations (node decimation [8]) to the model. Each triangulation is blocked by removing vertices at random and in the process the restrictions on the curvature are dropped so that the model flows into the wider class of combinatorial triangulations.…”
mentioning
confidence: 97%