Renormalization in combinatorially non-local field theories: the Hopf algebra of 2-graphs

TL;DR

This paper demonstrates that a broad class of non-local field theories, characterized by graph-structured interactions, can be understood through a Hopf algebra framework similar to local quantum field theories, enabling systematic renormalization analysis.

Contribution

It establishes that non-local field theories with graph-based interactions share the same Hopf algebra structure for renormalization as local theories, broadening algebraic methods' applicability.

Findings

Non-local theories can be analyzed using Hopf algebras.

Graph-structured interactions are as local as point interactions in renormalization.

Provides a foundation for studying non-perturbative aspects like Dyson-Schwinger equations.

Abstract

It is well known that the mathematical structure underlying renormalization in perturbative quantum field theory is based on a Hopf algebra of Feynman diagrams. A precondition for this is locality of the field theory. Consequently, one might suspect that non-local field theories such as matrix or tensor field theories cannot benefit from a similar algebraic understanding. Here I show that, on the contrary, the renormalization and perturbative diagramatics of a broad class of such field theories is based in the same way on a Hopf algebra. These theories are characterized by interaction vertices with graphs as external structure leading to Feynman diagrams which can be summed up under the concept of "2-graphs". From the renormalization perspective, such graph-like interactions are as much local as point-like interactions. They differ in combinatorial details as I exemplify with the central identity for the perturbative series of combinatorial correlation functions. This sets the stage for a systematic study of perturbative renormalization as well as non-perturbative aspects, e.g. Dyson-Schwinger equations, for a number of combinatorially non-local field theories with possible applications to quantum gravity, statistical models and more.