Parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions

We report on differential evolution for analytic continuation: a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation-based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly and experimentally relevant results are included for quantum Monte Carlo simulations of bulk He-4 below the superfluid transition temperature.

Automated summary

This paper presents a parameter-free evolutionary algorithm for analytic continuation to generate the dynamic structure factor from imaginary time correlation functions, achieving enhanced spectral fidelity and reduced CPU hours without the need for fine-tuning of algorithmic control parameters.