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

Nathan S Nichols1,2,3, Paul Sokol4, Adrian Del Maestro5,6

  • 1Data Science and Learning Division, Argonne National Laboratory, Argonne, Illinois 60439, USA.

Physical Review. E
|September 16, 2022
PubMed
Summary
This summary is machine-generated.

A new parameter-free evolutionary algorithm generates the dynamic structure factor from imaginary time correlation functions, enhancing spectral fidelity and reducing computation time for quantum many-body physics.

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Area of Science:

  • Quantum many-body physics
  • Computational physics

Background:

  • Analytic continuation of imaginary time correlation functions is a challenging problem.
  • Existing methods often require extensive parameter tuning, increasing computational cost.

Purpose of the Study:

  • To develop a parameter-free evolutionary algorithm for analytic continuation.
  • To improve spectral fidelity and reduce computational resources for calculating the dynamic structure factor.

Main Methods:

  • Differential evolution algorithm adapted for analytic continuation.
  • Embedding algorithmic control parameters within the evolutionary genome for self-optimization.
  • Application to quantum many-body systems, including bulk 4He.

Main Results:

  • Achieved enhanced spectral fidelity in dynamic structure factor calculations.
  • Reduced the required computation (CPU) hours compared to traditional methods.
  • Demonstrated accuracy on models with known dynamic structure factors and experimental relevance.

Conclusions:

  • The parameter-free differential evolution algorithm offers an efficient and robust solution for analytic continuation.
  • This method significantly improves the calculation of the dynamic structure factor in quantum many-body systems.
  • The approach is applicable to both theoretical models and experimental data analysis.