The Multi-Orientable Random Tensor Model, a Review
Adrian Tanasa
TL;DR
The paper reviews the multi-orientable (MO) random tensor model as a broader alternative to colored tensor models, detailing the $1/N$ expansion, large-$N$ limit, and the double scaling limit. It identifies melonic graphs as the leading sector and infinity graphs as the next-to-leading sector, and develops a thorough combinatorial framework (dipoles, chains, schemes) leading to dominant configurations described by rooted binary trees. The double scaling analysis combines $N\to\infty$ with critical coupling $\lambda\to\lambda_c$, yielding explicit scaling forms for two-, four-, and higher-point functions and revealing controlled, nonperturbative behavior through the parameter $\kappa$. The work also discusses probabilistic interpretations, renormalizability avenues, and future directions toward a continuum limit and connections with quantum gravity in higher dimensions.
Abstract
After its introduction (initially within a group field theory framework) in [Tanasa A., J. Phys. A: Math. Theor. 45 (2012), 165401, 19 pages, arXiv:1109.0694], the multi-orientable (MO) tensor model grew over the last years into a solid alternative of the celebrated colored (and colored-like) random tensor model. In this paper we review the most important results of the study of this MO model: the implementation of the $1/N$ expansion and of the large $N$ limit ($N$ being the size of the tensor), the combinatorial analysis of the various terms of this expansion and finally, the recent implementation of a double scaling limit.
