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SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions
by James Neuhaus, Nathan S. Nichols, Debshikha Banerjee, Benjamin Cohen-Stead, Thomas A. Maier, Adrian Del Maestro, Steven Johnston
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | James Neuhaus |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2407.04568v2 (pdf) |
| Code repository: | https://github.com/SmoQySuite/SmoQyDEAC.jl |
| Code version: | v1.1.1 |
| Code license: | MIT |
| Data repository: | https://zenodo.org/records/10407525 |
| Date accepted: | 2024-10-14 |
| Date submitted: | 2024-10-02 14:50 |
| Submitted by: | Neuhaus, James |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
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/.
List of changes
We've added two appendices. The first (appendix A) highlights the reduction of noise in the DEAC result as a user increases the number of total genomes run. The second (appendix C) shows the characteristics of overfitting.
Published as SciPost Phys. Codebases 39 (2024) , SciPost Phys. Codebases 39-r1.1 (2024)