Spontaneous chiral symmetry breaking in the massive Landau gauge: realistic running coupling

TL;DR

This paper advances the understanding of chiral symmetry breaking in QCD by employing a refined approximation scheme in the Curci-Ferrari model, incorporating realistic running coupling effects and comparing results with lattice data.

Contribution

It introduces a next-to-leading order approximation scheme that includes realistic running of parameters, improving upon previous models and enabling direct comparison with lattice QCD results.

Findings

Results for two-point functions agree well with lattice data.

Parameters of the model are strongly constrained by the data.

The approach successfully captures unquenched gluon and ghost propagators.

Abstract

We investigate the spontaneous breaking of chiral symmetry in QCD by means of a recently proposed approximation scheme in the Landau-gauge Curci-Ferrari model, which combines an expansion in the Yang-Mills coupling and in the inverse number of colors, without expanding in the quark-gluon coupling. The expansion allows for a consistent treatment of ultraviolet tails via renormalization group techniques. At leading order, it leads to the resummation of rainbow diagrams for the quark propagator, with, however, a trivial running of both the gluon mass and the quark-gluon coupling. In a previous work, by using a simple model for a more realistic running of these parameters, we could reproduce the known phenomenology of chiral symmetry breaking, including a satisfactory description of the lattice data for the quark mass function. Here, we get rid of this model-dependence by taking our approximation scheme to next-to-leading order. This allows us to consistently include the realistic running of the parameters and to access the unquenched gluon and ghost propagators to first nontrivial order, which we can compare to available lattice data for an even more stringent test of our approach. In particular, our results for the various two-point functions compare well with lattice data while the parameters of the model are strongly constrained.