论文标题
动态平均场理论中的统计误差估计及其扩展
Statistical error estimates in dynamical mean-field theory and extensions thereof
论文作者
论文摘要
我们采用折刀算法来分析通过伯特 - 盐分方程的统计量子蒙特卡洛误差的传播。这使我们能够估计易感性和动力学顶点近似计算的动态平均场理论计算的误差。我们发现，易感性的不同频率成分是不相关的，而自我能源的组件是相关的。为了提高相关矩阵的质量足够多，许多折刀垃圾箱是关键，而对于减少平均值足够多的蒙特卡洛测量值的标准误差是至关重要的。我们此外表明，即使在自我能源的情况下，有限的协方差也不会对分析延续产生重大影响。
We employ the jackknife algorithm to analyze the propagation of the statistical quantum Monte Carlo error through the Bethe--Salpeter equation. This allows us to estimate the error of dynamical mean-field theory calculations of the susceptibility and of dynamical vertex approximation calculations of the self-energy. We find that the different frequency components of the susceptibility are uncorrelated, whereas those of the self-energy are correlated. For improving the quality of the correlation matrix taking sufficiently many jackknife bins is key, while for reducing the standard error of the mean sufficiently many Monte Carlo measurements are necessary. We furthermore show that even in the case of the self-energy, the finite covariance does not have a sizable influence on the analytic continuation.
