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A data-driven convergence criterion for the D'Agostini (Richardson-Lucy)iterative unfolding is presented. It relies on the unregularized spectrum(infinite number of iterations), and allows a safe estimation of the bias andundercoverage induced by truncating the algorithm. In addition, situationswhere the response matrix is not perfectly known are also discussed, and showthat in most cases the unregularized spectrum is not an unbiased estimator ofthe true distribution. Whenever a bias is introduced, either by truncation ofby poor knowledge of the response, a way to retrieve appropriate coverageproperties is proposed.

A data-driven convergence criterion for iterative unfolding of smeared spectra