A Parameter Free Genetic Algorithm for Estimating the Dynamic Structure Factor at Zero and Finite Temperature

Abstract

We report on a self adaptive Differential Evolution for Analytic Continuation (DEAC) algorithm that can be used to reconstruct the dynamic structure factor from imaginary time density-density correlations. Our approach to this long-standing problem in quantum many-body physics achieves improved resolution of spectral features over earlier methods based on genetic algorithms. The need for fine-tuning of algorithmic control parameters is reduced by embedding them within the genome to be optimized. Benchmarks are presented for models where the dynamic structure factor is known exactly and we report new results for quantum Monte Carlo simulations of confined superfluid helium at low temperatures.

Nathan Nichols

Graduate Student in Materials Science

My research interests include low dimensional exotic phases of matter, quantum Monte Carlo algorithmic development, and machine learning for the quantum many-body problem.