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Although maximum entropy method (maxEnt method) is currently the standardalgorithm for extracting real frequency information from imaginary frequencyGreen function, still this method is beset with overfitting problem, whichmanifests itself as the spurious spikes in the resultant spectral functions. Toaddress this issue and motivated by the regularization techniques widely usedin machine learning and statistics, here we propose to add one moreregularization term into the original maxEnt loss function to suppress theseredundant spikes. The essence of this extra regularization term is to demandthat the resultant spectral functions should pay a price for being spiky. Wetest our algorithm with both artificial and real data, and find that spuriousspikes in the resultant spectral functions can be effectively suppressed bythis method.

Suppression of Overfitting in Extraction of Spectral Data from Imaginary Frequency Green Function Using Maximum Entropy Method