The 1/N expansion of multi-orientable random tensor models
S. Dartois, V. Rivasseau, A. Tanasa
TL;DR
This work extends the $1/N$ expansion from colored tensor models to the broader class of multi-orientable (m.o.) tensor models by introducing the i.i.d. m.o. framework and developing a generalized jacket formalism. It proves that the leading contributions at large $N$ arise from melonic graphs, even though m.o. graphs form a strictly larger graph class, with non-bipartite graphs contributing subleading terms due to non-orientable jackets. The authors derive the $1/N$ scaling $A(\mathcal{G})=\lambda^{v_{\mathcal{G}}}N^{3-\varpi(\mathcal{G})}$ and show that $\varpi(\mathcal{G})=0$ defines the melonic sector, which maps to 3-ary trees and is countable via Catalan numbers. These results broaden the applicability of large-$N$ techniques in random tensor models and pave the way for future studies of m.o. tensor field theories, renormalization, and GFT generalizations.
Abstract
Multi-orientable group field theory (GFT) has been introduced in A. Tanasa, J. Phys. A 45 (2012) 165401, arXiv:1109.0694, as a quantum field theoretical simplification of GFT, which retains a larger class of tensor graphs than the colored one. In this paper we define the associated multi-orientable identically independent distributed multi-orientable tensor model and we derive its 1/N expansion. In order to obtain this result, a partial classification of general tensor graphs is performed and the combinatorial notion of jacket is extended to the multi-orientable graphs. We prove that the leading sector is given, as in the case of colored models, by the so-called melon graphs.
