  We propose a new iterative unfolding method for experimental data, making use
of a regularization function. The use of this function allows one to build an
improved normalization procedure for Monte Carlo spectra, unbiased by the
presence of possible new structures in data. We are able to unfold, in a
dynamically stable way, data spectra which can be strongly affected by
fluctuations in the background subtraction and simultaneously reconstruct
structures which were not initially simulated. This method also allows one to
control the amount of correlations introduced between the bins of the unfolded
spectrum, when the transfers of events correcting the systematic detector
effects are performed.
