Multiscale Monte Carlo equilibration: Two-color QCD with two fermion flavors

TL;DR

This paper applies a multiscale thermalization algorithm to two-color QCD with two fermion flavors, addressing numerical instabilities and demonstrating rapid thermalization through a proposed solution and effective action matching.

Contribution

It extends a multiscale thermalization method to dynamical fermion QCD, identifies causes of instabilities, and proposes a solution for efficient rethermalization.

Findings

Rapid thermalization achieved with the proposed method.

Numerical instabilities linked to near-zero Dirac modes are mitigated.

Effective RG matching conditions improve rethermalization rates.

Abstract

We demonstrate the applicability of a recently proposed multiscale thermalization algorithm to two-color quantum chromodynamics (QCD) with two mass-degenerate fermion flavors. The algorithm involves refining an ensemble of gauge configurations that had been generated using a renormalization group (RG) matched coarse action, thereby producing a fine ensemble that is close to the thermalized distribution of a target fine action; the refined ensemble is subsequently rethermalized using conventional algorithms. Although the generalization of this algorithm from pure Yang-Mills theory to QCD with dynamical fermions is straightforward, we find that in the latter case, the method is susceptible to numerical instabilities during the initial stages of rethermalization when using the hybrid Monte Carlo algorithm. We find that these instabilities arise from large fermion forces in the evolution, which are attributed to an accumulation of spurious near-zero modes of the Dirac operator. We propose a simple strategy for curing this problem, and demonstrate that rapid thermalization--as probed by a variety of gluonic and fermionic operators--is possible with the use of this solution. In addition, we study the sensitivity of rethermalization rates to the RG matching of the coarse and fine actions, and identify effective matching conditions based on a variety of measured scales.