Asymptotic expansion of the multi-orientable random tensor model
Eric Fusy, Adrian Tanasa
TL;DR
This work analyzes the large-$N$ expansion of the three-dimensional multi-orientable (MO) tensor model by extending Gurău–Schaeffer scheme techniques to MO graphs. It defines melon-free cores and reduced schemes, proving finiteness of schemes at fixed degree $\delta$ and identifying dominant schemes associated with rooted binary trees; it then derives generating functions and precise asymptotics for the number of rooted MO-graphs of degree $\delta$, showing planarity becomes overwhelmingly likely in the dominant sector. The results elucidate the combinatorial structure of MO graphs, expose the role of broken chain-vertices in determining singular behavior, and provide a concrete path toward a double scaling limit and higher-dimensional generalizations. These findings have potential impact on understanding universality and phase structure in tensor models and on connecting tensor combinatorics with analytic combinatorics methods.
Abstract
Three-dimensional random tensor models are a natural generalization of the celebrated matrix models. The associated tensor graphs, or 3D maps, can be classified with respect to a particular integer or half-integer, the degree of the respective graph. In this paper we analyze the general term of the asymptotic expansion in N, the size of the tensor, of a particular random tensor model, the multi-orientable tensor model. We perform their enumeration and we establish which are the dominant configurations of a given degree.
