Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach
Abstract
A new algorithm for analytic continuation of noisy quantum Monte Carlo (QMC) data from the Matsubara domain to real frequencies is proposed. Unlike the widely used maximum-entropy (MaxEnt) procedure, our method is linear with respect to input data and can therefore be applied to off-diagonal components of a thermal Green's function, or to a self-energy function. The latter possibility is used to analyze QMC results for the half-filled single-band Hubbard model on a Bethe lattice at a low temperature. Our method qualitatively resolves peaks near the inner edges of the Hubbard bands in the vicinity of a Mott transition, whereas a MaxEnt procedure does not. An existence of such structures has been clearly established before in a high-precision D-DMRG calculation by Karski et al. We also analyze a stability of the new method subject to changes of adjustable parameters.
Cite
@article{arxiv.cond-mat/0612233,
title = {Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach},
author = {I. S. Krivenko and A. N. Rubtsov},
journal= {arXiv preprint arXiv:cond-mat/0612233},
year = {2011}
}
Comments
14 pages A major revision of Sections V and VI, proofreading