Bayesian parametric analytic continuation of Green's functions

摘要: Bayesian parametric analytic continuation (BPAC) is proposed for the of noisy imaginary-time Green's function data as, e.g., obtained by continuous-time quantum Monte Carlo simulations (CTQMC). Within BPAC, spectral inferred from a suitable set parametrized basis functions. model comparison then allows to assess reliability different parametrizations. The required evidence integrals such are determined nested sampling. Compared maximum entropy method (MEM), routinely used CTQMC data, presented approach infer whether support specific structures function. We demonstrate capability BPAC in terms an Anderson impurity (AIM) that shows generalized Kondo scenario and compare reconstruction MEM as well real-time fork tensor product state solver where no required. Furthermore, we present combination its application AIM arising ab initio treatment SrVO$_3$.