Stochastic pole expansion method

TL;DR

This paper introduces a stochastic pole expansion method for analytic continuation of Green's functions, accurately capturing spectral features and robust against noise, advancing quantum many-body spectral analysis.

Contribution

The paper presents a novel stochastic pole expansion technique with constrained and adaptive sampling, improving spectral function extraction from Green's functions over traditional methods.

Findings

Accurately reproduces key spectral features including sharp peaks and band edges.

Demonstrates robustness to noisy and incomplete data.

Outperforms traditional maximum entropy methods in resolution and stability.

Abstract

In this paper, we propose a new analytic continuation method to extract real frequency spectral functions from imaginary frequency Green's functions of quantum many-body systems. This method is based on the pole representation of Matsubara Green's function and a stochastic sampling procedure is utilized to optimize the amplitudes and locations of poles. In order to capture narrow peaks and sharp band edges in the spectral functions, a constrained sampling algorithm and a self-adaptive sampling algorithm are developed. To demonstrate the usefulness and performance of the new method, we at first apply it to study the spectral functions of representative fermionic and bosonic correlators. Then we employ this method to tackle the analytic continuation problems of matrix-valued Green's functions. The synthetic Green's functions, as well as realistic correlation functions from finite temperature quantum many-body calculations, are used as input. The benchmark results demonstrate that this method is capable of reproducing most of the key characteristics in the spectral functions. The sharp, smooth, and multi-peak features in both low-frequency and high-frequency regions of spectral functions could be accurately resolved, which overcomes one of the main limitations of the traditional maximum entropy method. More importantly, it exhibits excellent robustness with respect to noisy and incomplete input data. The causality of spectral function is always satisfied even in the presence of sizable noises. As a byproduct, this method could derive a fitting formula for the Matsubara data, which provides a compact approximation to the many-body Green's functions. Hence, we expect that this new method could become a pivotal workhorse for numerically analytic continuation and be broadly useful in many applications.