Hi, on Dec 9th we had shown a preliminary unfolded m_X spectrum, where some things still needed to be changed. 1) At that time the unfolding treated the data as if it just had normal sqrt(N)-errors on each bin. We changed this such that now the actual error from the fit is taken. 2) We are no longer performing the fit with our equidistant binning. Instead, we just feed in the results from fits with the default binning for vubcomp, vcbcomp, errvcbcomp, othcomp and errothcomp and do all the other things with our binning (m_ES-subtraction, bkgd-subtraction, ...). 3) Third, we now unfold taking into account the multiplicity categories. For this, we use the m_X spectra from data and signal MC divided up into the five usual multiplicity categories. This is done as follows: * We determine weights for the reconstructed signal MC such that its relative multiplicity category population is the same as in data, i.e. we apply the following weight to signal MC to multiplicity category (mc) i w_mc^i = dr_mc^i / br_mc^i where dr_mc^i is the relative population of multiplicity category i in data and br_mc^i is the relative population of reconstructed multiplicity category i in signal MC. * The (m_X!) detector response matrix (that is used for the unfolding) is determined for each multiplicity category separately A_mx^i and these matrices are added up with the same weight w_mc^i per multiplicity category that is applied to the reconstructed signal MC: A_mx = A_mx^i * w_mc^i (sum over i) to give the detector response matrix that is used for the unfolding. * We then determine the weights for the generated multiplicity categories by requiring that the resulting spectrum (i.e. reweighted generated multiplicity category spectrum x_mc^rew) yields the reweighted reconstructed multiplicity category spectrum b_mc^rew when applying the multiplicity category detector response matrix, i.e. the requirement is A_mc x_mc^rew = b_mc^rew. (same principle as for m_X, A_mc is the matrix mediating between the generated and the reconstructed distribution A_mc x_mc = b_mc with x_mc being a vector with the population of the generated multiplicity categories, b_mc being the vector for the reco mc.) We use the weights we obtained this way for the generated multiplicity categories to reweight the generated m_X spectrum, which we use for the unfolding. Here is the current plot: http://www.slac.stanford.edu/~kerstin/mult24_4.eps where the blue distribution is the reweighted generated m_X distribution that is used for the initialization, the black data points are the unfolded spectrum and the green distribution is the scaled measured spectrum, which is unfolded. Assuming that the data sample contains about 89 Mio BBbar pairs and taking the branching fractions from PDG assuming equal numbers of neutral and charged B one would expect to see about 24000 pis and 35000 rhos. We do not see any inconsistency between these numbers and the unfolded spectrum. We will be glad to get some feedback on this as well as your opinion concerning the dominant systematic errors (what they are and how they should be evaluated). Kerstin