Coarse graining methods for spin net and spin foam models

Abstract

We undertake first steps in making a class of discrete models of quantum gravity, spin foams, accessible to a large-scale analysis by numerical and computational methods. In particular, we apply the Migdal–Kadanoff and tensor network renormalization (TNR) schemes to spin net and spin foam models based on finite Abelian groups and introduce 'cutoff models' to probe the fate of gauge symmetries under various such approximated renormalization group flows. For the TNR analysis, a new Gauß constraint preserving algorithm is introduced to improve numerical stability and aid physical interpretation. We also describe the fixed point structure and establish the equivalence of certain models.