  Although maximum entropy method (maxEnt method) is currently the standard
algorithm for extracting real frequency information from imaginary frequency
Green function, still this method is beset with overfitting problem, which
manifests itself as the spurious spikes in the resultant spectral functions. To
address this issue and motivated by the regularization techniques widely used
in machine learning and statistics, here we propose to add one more
regularization term into the original maxEnt loss function to suppress these
redundant spikes. The essence of this extra regularization term is to demand
that the resultant spectral functions should pay a price for being spiky. We
test our algorithm with both artificial and real data, and find that spurious
spikes in the resultant spectral functions can be effectively suppressed by
this method.
