论文标题
通过机器学习技术对自我能源的分析延续
Analytic continuation of the self-energy via Machine Learning techniques
论文作者
论文摘要
我们开发了一种新型的分析延续方法，用于在动态平均场理论（QMC+DMFT）中通过量子蒙特卡洛模拟计算的Matsubara结构域上的自我能量。与过去30年中采用的最大熵（最大）程序不同，我们的方法基于机器学习（ML）技术，并结合迭代扰动理论杂质求解器（IPT+DMFT）的迭代扰动理论杂质求解器。 ML的输入和输出训练数据集分别从Matsubara和真实频率域上的IPT+DMFT计算得出。实际频率上的QMC+DMFT自我能源是通过（通常是嘈杂）输入QMC+DMFT自我能源在Matsubara域和受过训练的ML内核上确定的。我们的方法与ML训练数据集的偏置以及Maxen方法中存在的拟合参数无关。我们证明了该方法在正方形晶格上测试床沮丧的哈伯德模型上的效率。
We develop a novel analytic continuation method for self-energies on the Matsubara domain as computed by quantum Monte Carlo simulations within dynamical mean field theory (QMC+DMFT). Unlike a maximum entropy (maxEn) procedure employed for the last thirty years, our approach is based on a machine learning (ML) technique in combination with the iterative perturbative theory impurity solver of the dynamical mean field theory self-consistent process (IPT+DMFT). The input and output training datasets for ML are simultaneously obtained from IPT+DMFT calculations on Matsubara and real frequency domains, respectively. The QMC+DMFT self-energy on real frequencies is determined from the -- usually noisy -- input QMC+DMFT self-energy on the Matsubara domain and the trained ML kernel. Our approach is free from both, bias of ML training datasets and from fitting parameters present in the maxEn method. We demonstrate the efficiency of the method on the testbed frustrated Hubbard model on the square lattice.
