数值重整化群中提高谱分辨率的自适应加宽
Adaptive broadening to improve spectral resolution in the numerical renormalization group
摘要：
我们提出了一个自适应的方案，加宽的离散光谱数据的数值重整化群（NRG）计算，以提高在有限能量的动力学性质的分辨率。虽然传统的计划overbroadens窄功能在大的频率，通过扩大离散权重与恒定宽度的对数频率，我们的计划扩大了每个离散的贡献单独的基础上，其灵敏度的z位移的对数离散化间隔。我们证明，自适应加宽更好地解决了各种功能，在非相互作用和相互作用的模型在可比的计算成本。对于多波段计算中通常需要的较粗离散化，分辨率增强更为重要。在低于温度的能量标度的低频下，离散NRG数据必然需要在线性标度上加宽。在这里，我们提供了一种方法，最大限度地减少这些加宽内核之间的过渡伪影。
英文摘要：
We propose an adaptive scheme of broadening the discrete spectral data from numerical renormalization group (NRG) calculations to improve the resolution of dynamical properties at finite energies. While the conventional scheme overbroadens narrow features at large frequency by broadening discrete weights with constant width in log-frequency, our scheme broadens each discrete contribution individually based on its sensitivity to a z-shift in the logarithmic discretization intervals. We demonstrate that the adaptive broadening better resolves various features in non-interacting and interacting models at comparable computational cost. The resolution enhancement is more significant for coarser discretization as typically required in multi-band calculations. At low frequency below the energy scale of temperature, the discrete NRG data necessarily needs to be broadened on a linear scale. Here we provide a method that minimizes transition artefacts in between these broadening kernels.
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