DCA$^+$: Dynamical Cluster Approximation with continuous lattice self-energy
Peter Staar, Thomas Maier, Thomas C. Schulthess

TL;DR
The paper introduces DCA$^+$, an improved dynamical cluster approximation method that uses a continuous lattice self-energy to enhance convergence, reduce shape dependence, and mitigate the sign problem in quantum many-body simulations.
Contribution
DCA$^+$ extends the DCA by incorporating a continuous lattice self-energy, solving shape dependence issues and improving convergence in strongly correlated system simulations.
Findings
DCA$^+$ shows monotonic convergence of self-energy and pseudogap temperature with cluster size.
The method reduces the sign problem, enabling larger cluster simulations.
It allows more accurate extrapolations to the infinite cluster size limit.
Abstract
The dynamical cluster approximation (DCA) is a systematic extension beyond the single site approximation in dynamical mean field theory (DMFT), to include spatially non-local correlations in quantum many-body simulations of strongly correlated systems. We extend the DCA with a continuous lattice self-energy in oder to achieve better convergence with cluster size. The new method, which we call DCA, cures the cluster shape dependence problems of the DCA, without suffering from causality violations of previous attempts to interpolate the cluster self-energy. A practical approach based on standard inference techniques is given to deduce the continuous lattice self-energy from an interpolated cluster self-energy. We study the pseudogap region of a hole-doped two-dimensional Hubbard model and find that in the DCA algorithm, the self-energy and pseudo-gap temperature converge…
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