RPA for Light-Front Hamiltonian Field Theory

TL;DR

This paper introduces a self-consistent RPA method as an effective Hamiltonian approach in Light-Front Field Theory, demonstrating its application to the massive Schwinger model to derive and numerically solve a new bound state equation.

Contribution

It presents a novel RPA-based method tailored for Light-Front Field Theory, specifically applied to the massive Schwinger model for bound state analysis.

Findings

Derived a new bound state equation for the massive Schwinger model

Numerically solved the bound state equation

Showed the effectiveness of RPA in Light-Front Hamiltonian methods

Abstract

A self-consistent random phase approximation (RPA) is proposed as an effective Hamiltonian method in Light-Front Field Theory (LFFT). We apply the general idea to the light-front massive Schwinger model to obtain a new bound state equation and solve it numerically.