Analytic continuation from limited noisy Matsubara data
arXiv:2202.09719 · doi:10.1016/j.jcp.2022.111549
Abstract
This note proposes a new algorithm for estimating spectral function from limited noisy Matsubara data. We consider both the molecule and condensed matter cases. In each case, the algorithm constructs an interpolant of the Matsubara data and uses conformal mapping and Prony's method to estimate the spectral function. Numerical results are provided to demonstrate the performance of the algorithm.
References in corpus (11)
- The AAA algorithm for rational approximation
- A reliable Pade analytical continuation method based on a high accuracy symbolic computation algorithm
- Nevanlinna Analytical Continuation
- Maximum entropy formalism for the analytic continuation of matrix-valued Green's functions
- Implementation of the Maximum Entropy Method for Analytic Continuation
- Numerical analytic continuation: Answers to well-posed questions
- Analytical Continuation of Matrix-Valued Functions: Carathéodory Formalism
- Analytic continuation by averaging Padé approximants
- Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation
- : Implementation of the Stochastic Optimization Method for Analytic Continuation
- Bayesian parametric analytic continuation of Green's functions