Worm algorithm for the O(2N) Gross-Neveu model

TL;DR

This paper introduces a worm algorithm for the lattice O(2N) Gross-Neveu model with Wilson fermions, enabling efficient simulation of topological boundary conditions and correlation functions of bound states involving Majorana fermions.

Contribution

The paper develops and applies a worm algorithm to simulate the O(2N) Gross-Neveu model, allowing for the sampling of complex correlation functions and boundary conditions.

Findings

Successful simulation of fluctuating topological boundary conditions.

Extension of the worm algorithm to bound state correlation functions.

Initial results demonstrating the algorithm's effectiveness.

Abstract

We study the lattice O(2N) Gross-Neveu model with Wilson fermions in the fermion loop formulation. Employing a worm algorithm for an open fermionic string, we simulate fluctuating topological boundary conditions and use them to tune the system to the critical point. We show how the worm algorithm can be extended to sample correlation functions of bound states involving an arbitrary number of Majorana fermions and present first results.