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Related papers: New Limits for Large $N$ Matrix and Tensor Models:…

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Random matrices in the large N expansion and the so-called double scaling limit can be used as toy models for quantum gravity: 2D quantum gravity coupled to conformal matter. This has generated a tremendous expansion of random matrix…

Mathematical Physics · Physics 2014-10-08 Jean Zinn-Justin

Random matrix models encode a theory of random two dimensional surfaces with applications to string theory, conformal field theory, statistical physics in random geometry and quantum gravity in two dimensions. The key to their success lies…

Mathematical Physics · Physics 2012-09-21 Razvan Gurau

We study a connection between random tensors and random matrices through $U(\tau)$ matrix models which generate fully packed, oriented loops on random surfaces. The latter are found to be in bijection with a set of regular edge-colored…

High Energy Physics - Theory · Physics 2014-11-27 Valentin Bonzom , Frédéric Combes

The $D$-colored version of tensor models has been shown to admit a large $N$-limit expansion. The leading contributions result from so-called melonic graphs which are dual to the $D$-sphere. This is a note about the Schwinger-Dyson…

High Energy Physics - Theory · Physics 2015-09-08 Dine Ousmane Samary , Carlos I. Pérez-Sánchez , Fabien Vignes-Tourneret , Raimar Wulkenhaar

Random tensor models for a generic complex tensor generalize matrix models in arbitrary dimensions and yield a theory of random geometries. They support a 1/N expansion dominated by graphs of spherical topology. Their Schwinger Dyson…

High Energy Physics - Theory · Physics 2015-06-04 Razvan Gurau

Extending early work, we formulate the large N matrix mechanics of general bosonic, fermionic and supersymmetric matrix models, including Matrix theory: The Hamiltonian framework of large N matrix mechanics provides a natural setting in…

High Energy Physics - Theory · Physics 2014-11-18 M. B. Halpern , C. Schwartz

In this paper we identify and analyze in detail the subleading contributions in the 1/N expansion of random tensors, in the simple case of a quartically interacting model. The leading order for this 1/N expansion is made of graphs, called…

High Energy Physics - Theory · Physics 2015-06-16 Stephane Dartois , Razvan Gurau , Vincent Rivasseau

We demonstrate that random tensors transforming under rank-$5$ irreducible representations of $\mathrm{O}(N)$ can support melonic large $N$ expansions. Our construction is based on models with sextic ($5$-simplex) interaction, which…

Mathematical Physics · Physics 2022-01-20 Sylvain Carrozza , Sabine Harribey

We define in this paper a class of three indices tensor models, endowed with $O(N)^{\otimes 3}$ invariance ($N$ being the size of the tensor). This allows to generate, via the usual QFT perturbative expansion, a class of Feynman tensor…

Mathematical Physics · Physics 2016-10-11 Sylvain Carrozza , Adrian Tanasa

Amplitudes of ordinary tensor models are dominated at large $N$ by the so-called melonic graph amplitudes. Enhanced tensor models extend tensor models with special scalings of their interactions which allow, in the same limit, that the…

High Energy Physics - Theory · Physics 2018-12-26 Joseph Ben Geloun , Reiko Toriumi

In this note, we study a melonic tensor model in $d$ dimensions based on three-index Dirac fermions with a four-fermion interaction. Summing the melonic diagrams at strong coupling allows one to define a formal large-$N$ saddle point in…

High Energy Physics - Theory · Physics 2018-04-04 Shiroman Prakash , Ritam Sinha

An U(N)-invariant matrix model with d matrix variables is studied. It was shown that in the limit $N\to \infty $ and $d\to 0$ the model describes the knot diagrams. We realize the free partition function of the matrix model as the…

Quantum Algebra · Mathematics 2007-05-23 Martin Grothaus , Ludwig Streit , Igor V. Volovich

A class of matrix models which arises as partition function in U(N) Chern-Simons matter theories on three sphere is investigated. Employing the standard technique of the 1/N expansion we solve the system beyond the planar limit. In…

High Energy Physics - Theory · Physics 2019-12-06 Shuichi Yokoyama

Colored tensor models generalize matrix models in higher dimensions. They admit a 1/N expansion dominated by spherical topologies and exhibit a critical behavior strongly reminiscent of matrix models. In this paper we generalize the colored…

High Energy Physics - Theory · Physics 2015-05-28 Razvan Gurau

Tensor models are measures for random tensors. They generalise matrix models and were developed to study random geometry in arbitrary dimension. Moreover, they are strongly connected to quantum gravity theories as additionally to the…

Mathematical Physics · Physics 2017-06-26 Thibault Delepouve

In this paper we extend the 1/N expansion introduced in [1] to group field theories in arbitrary dimension and prove that only graphs corresponding to spheres S^D contribute to the leading order in the large N limit.

General Relativity and Quantum Cosmology · Physics 2019-08-17 Razvan Gurau , Vincent Rivasseau

The large N limit of the hermitian matrix model in three and four Euclidean space-time dimensions is studied with the help of the approximate Renormalization Group recursion formula. The planar graphs contributing to wave function, mass and…

High Energy Physics - Theory · Physics 2009-10-28 Gabriele Ferretti

Tensor models generalize matrix models and generate colored triangulations of pseudo-manifolds in dimensions $D\geq 3$. The free energies of some models have been recently shown to admit a double scaling limit, i.e. large tensor size $N$…

High Energy Physics - Theory · Physics 2014-09-12 Valentin Bonzom , Razvan Gurau , James P. Ryan , Adrian Tanasa

This thesis focuses on renormalization of quantum field theories. Its first part considers three tensor models in three dimensions, a Fermionic quartic with tensors of rank-3 and two Bosonic sextic, of ranks 3 and 5. We rely upon the…

High Energy Physics - Theory · Physics 2020-10-16 Nicolas Delporte

Neural networks that satisfy invariance with respect to input permutations have been widely studied in machine learning literature. However, in many applications, only a subset of all input permutations is of interest. For heterogeneous…

Machine Learning · Computer Science 2023-02-28 Dmitry Rybin , Ruoyu Sun , Zhi-Quan Luo