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We propose an adaptive scheme of broadening the discrete spectral data fromnumerical renormalization group (NRG) calculations to improve the resolution ofdynamical properties at finite energies. While the conventional schemeoverbroadens narrow features at large frequency by broadening discrete weightswith constant width in log-frequency, our scheme broadens each discretecontribution individually based on its sensitivity to a z-shift in thelogarithmic discretization intervals. We demonstrate that the adaptivebroadening better resolves various features in non-interacting and interactingmodels at comparable computational cost. The resolution enhancement is moresignificant for coarser discretization as typically required in multi-bandcalculations. At low frequency below the energy scale of temperature, thediscrete NRG data necessarily needs to be broadened on a linear scale. Here weprovide a method that minimizes transition artefacts in between thesebroadening kernels.

Adaptive broadening to improve spectral resolution in the numerical renormalization group