Spectral Densities from Dynamic Density-Matrix Renormalization

TL;DR

This paper introduces a nonlinear deconvolution method to extract spectral densities from dynamic density-matrix renormalization data, improving the analysis of features like the Kondo peak and Hubbard satellites.

Contribution

It proposes a novel nonlinear deconvolution scheme that reduces bias in retrieving spectral densities from DDMRG data, demonstrated on the Anderson model.

Findings

Hubbard satellites are strongly asymmetric

Deconvolution improves spectral feature analysis

Method effectively retrieves spectral densities

Abstract

Dynamic density-matrix renormalization provides valuable numerical information on dynamic correlations by computing convolutions of the corresponding spectral densities. Here we discuss and illustrate how and to which extent such data can be deconvolved to retrieve the wanted spectral densities. We advocate a nonlinear deconvolution scheme which minimizes the bias in the ansatz for the spectral density. The procedure is illustrated for the line shape and width of the Kondo peak (low energy feature) and for the line shape of the Hubbard satellites (high energy feature) of the single impurity Anderson model. It is found that the Hubbard satellites are strongly asymmetric.