SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

TL;DR

The paper introduces SmoQyDEAC.jl, a Julia package implementing the Differential Evolution Analytic Continuation algorithm for transforming noisy imaginary time correlation data into real frequency spectra, supporting various data types and error treatments.

Contribution

It provides a new Julia-based implementation of the DEAC algorithm with support for fermionic and bosonic functions, covariance error handling, and comprehensive benchmarking against existing methods.

Findings

Accurately continues noisy data to real frequencies.

Outperforms maximum entropy and stochastic methods in benchmarks.

Supports diverse correlation functions and error models.

Abstract

We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.