Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach

Physics – Condensed Matter – Strongly Correlated Electrons

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

14 pages A major revision of Sections V and VI, proofreading

Scientific paper

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Analytic Continuation of Quantum Monte Carlo Data: Optimal Stochastic Regularization Approach will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-440413

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.