Improved Treatment of Frequency Sums in Propagator-Renormalized Perturbation Theories

TL;DR

This paper introduces a scalable parallel algorithm for calculating the self-energy in finite temperature perturbation theory, improving accuracy by better handling high frequency tails in Green's functions.

Contribution

The paper presents a novel parallel algorithm that accurately computes the self-energy in lattice models, addressing issues with high frequency tail truncation in traditional methods.

Findings

The algorithm achieves high accuracy in self-energy calculations.

It is scalable for large lattice models.

Comparison shows improved results over traditional truncation methods.

Abstract

We present a massively parallel algorithm for calculating the self-energy in self-consistent finite temperature perturbation theory for lattice models. The algorithm uses analytic functions with appropriate asymptotic high frequency behavior and fast Fourier transforms to accurately calculate the self-energy at low-frequency. Traditional methods that truncate the high frequency tails of the temperature Green's function lead to `contamination' of the low-frequency behavior of the self-energy. Our algorithm is both accurate and scalable. We compare results for the Hubbard model using various techniques for handling the high frequency tails of the temperature Green's function.