Diagrammatic Monte Carlo approach for diagrammatic extensions of dynamical mean-field theory -- convergence analysis of the dual fermion technique
Jan Gukelberger, Evgeny Kozik, Hartmut Hafermann

TL;DR
This paper uses a stochastic diagrammatic Monte Carlo method to analyze the convergence of the dual-fermion expansion for the 2D Hubbard model, revealing rapid convergence at high temperatures and challenges at low temperatures with magnetic correlations.
Contribution
It provides a comprehensive convergence analysis of the dual-fermion series including all diagram topologies, benchmarking against exact methods, and assessing the validity of common approximations.
Findings
Dual-fermion series converges quickly at high temperatures.
Slower convergence and divergence occur at low temperatures near magnetic transitions.
Particle-hole ladder approximation remains accurate in regimes with magnetic correlations.
Abstract
The dual-fermion approach provides a formally exact prescription for calculating properties of a correlated electron system in terms of a diagrammatic expansion around dynamical mean-field theory (DMFT). Most practical implementations, however, neglect higher-order interaction vertices beyond two-particle scattering in the dual effective action and further truncate the diagrammatic expansion in the two-particle scattering vertex to a leading-order or ladder-type approximation. In this work we compute the dual-fermion expansion for the two-dimensional Hubbard model including all diagram topologies with two-particle interactions to high orders by means of a stochastic diagrammatic Monte Carlo algorithm. We benchmark the obtained self-energy against numerically exact Diagrammatic Determinant Monte Carlo simulations to systematically assess convergence of the dual-fermion series and the…
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Taxonomy
TopicsPhysics of Superconductivity and Magnetism · Advanced Condensed Matter Physics · Quantum and electron transport phenomena
