Designing weight regularizations based on Lefschetz thimbles to stabilize complex Langevin

TL;DR

This paper introduces a novel weight regularization technique based on Lefschetz thimbles to improve the stability and correctness of the complex Langevin method in solving models with sign problems.

Contribution

It proposes a new regularization approach inspired by Lefschetz thimbles to correct biases in complex Langevin simulations, enhancing their reliability.

Findings

Successfully applied to SU(N) Polyakov chain model

Effective in scalar models like cosine and one-link models

Broadly stabilizes CL method across various coupling regimes

Abstract

The complex Langevin (CL) method shows significant potential in addressing the numerical sign problem. Nonetheless, it often produces incorrect results when used without any stabilization techniques. Leveraging insights from previous research that links Lefschetz thimbles and CL, we explore a strategy to regularize the CL method to address this issue of incorrect convergence. Specifically, we implement weight regularizations inspired by the associated Lefschetz thimble structure and correct the bias to retrieve the correct results of the original theory. We demonstrate the effectiveness of this approach by solving the SU(N) Polyakov chain model and various scalar models, including the cosine model and the one-link model, across a broad range of couplings where the CL method previously failed. We also discuss the potential application of these insights to gauge theories in practical scenarios.