The results in the last column were obtained without taking into account the statistical errors from the templates. We can see that as the number of events increase the absolute value of the errors tend to the case when the errors from the templates are zero. The central values fluctuates a lot. An extra sample with L = 2000 fb^-1 (34000 events of mu-mu-h) would be the minimum (Desch-Kuhl used 5000 fb^-1 and split in data and Monte Carlo) . Presently there is 1000 fb^-1 reconstructed. We can use 500 fb^-1 (which is what one expects in reality) for the data and the other 500 fb^-1 with the extra 2000 fb^-1 that we request. I would prefer more, but we have to see that the time scale is short.
Besides that we should try to get an extra 1000 fb^-1 for likelihood or other method for signal-background separation.
What do you think?
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