Hi, 
yes this should be the one with lepton cuts. However I am still
recomputing the S/P ratios by using Antonio's latest package and will
repeat the exercise again. Wolfgang, why should we apply 50% and 100%
correlations? The mX bins should be statistically independent, no? 
Concezio. 

Il giorno gio, 27-07-2006 alle 11:27 +0200, Wolfgang Menges ha scritto:
> Hi Concezio,
> 
> 	as Heiko said, very promissing. Can you run with 50% and 100% correlation?
> 
> Cheers,
> 
> 	Wolfgang
> 
> Concezio Bozzi wrote:
> > Hi all, 
> > 
> > I run a test to estimate the systematic uncertainty due to the
> > uncertainty on S/P in mES data fits in the following way: 
> > 
> > 1) Take the S/P ratios determined as 
> > 
> > S/P(data_enriched) = [S/P(MC_enriched) / S/P(MC_depleted)] * S/P
> > (data_depleted)
> > 
> > I have used the values which I have been recently playing with, i.e. 
> > #mx_l mx_h  corr   err_corr
> > 0.00 1.55 1.499 +- 0.495
> > 1.55 1.90 2.688 +- 0.655
> > 1.90 2.20 1.801 +- 0.296
> > 2.20 2.50 1.896 +- 0.611
> > 2.50 2.80 1.165 +- 0.468
> > 2.80 3.10 0.637 +- 0.311
> > 3.10 3.40 19.367+- 34.585
> > 3.40 3.70 1.524 +- 1.610
> > 3.70 4.20 8.180 +- 31.833
> > 4.20 5.00 0.555 +- 6.639
> > 
> > No attempt to fit a n-th order polynomial, just take the values as they
> > come out of the single bin-by-bin fits on data depleted, MC enriched and
> > depleted. 
> > Note that the relative errors are quite large (e.g. 33% on the first
> > bin, 25% on the second, 22% on the third, 32% on the 4th, higher and
> > higher as mX increases). 
> > 
> > 2) Randomize simultaneously the 10 above values according to a gaussian
> > distribution whose mean is the correction (column corr) and whose sigma
> > is the uncertainty (err_corr). The random number is of course different
> > for each mX bin. 
> > 
> > 3) Fit with VVF by using the randomized S/P of point 2) 
> > 
> > 4) go to 2), change the random seed, repeat 100 times
> > 
> > Results of the 100 jobs are in 
> > http://www.slac.stanford.edu/~bozzi/scra/Ibu_SP_*
> > *=1,...,100
> > 
> > Take the (width/mean) ratio of the resulting 100 fits as systematic
> > uncertainty: 
> > 
> > yakut02(~:) grep "BRBR           " ~bozzi/scra/Ibu_SP_*/*dat | awk
> > 'BEGIN{sum=0; sum2=0}{sum+= $3; sum2+=$3*$3; num++}END{print sum/num;
> > print sqrt(sum2/num-sum*sum/num/num)}'
> > 0.0291231
> > 0.00188286
> > 
> > The relative uncertainty is 0.00188/0.02912 = 6.46% i.e. 3.2% on Vub. 
> > This is somewhat lower than a naive argument which can be used (see
> > below) to give the error on the fitted Vub events in the first bin and
> > which give about twice (13.2%) the error on BRBR. I think the reason for
> > this is that the errors on the first 4 bins are comparable, which
> > reduces the lever arm and therefore the variation in the first bin. 
> > 
> > Quite promising, isn't it? 
> > 
> > Concezio. 
> > 
> > 
> > PS: here is the naive argument on the uncertainty on the number of
> > signal events in the first bin, which translates in the uncertainty on
> > BRBR. We have
> > 
> > N_signal = N_data - N_argus - N_peaking 
> > 
> > N_peaking = N_signal * 1/corr
> > (corr is the S/P correction factor)
> > 
> > Solving for N_signal:
> > 
> > N_signal = [corr / (1+ corr)] * [N_data - N_argus] 
> > 
> > Error propagation gives: 
> > 
> > delta(N_signal) / N_signal = [delta(corr) / corr] / (1+corr) 
> > 
> > Taking corr = 1.499, delta(corr)/corr = 0.33 we get 
> > 
> > delta(N_signal) / N_signal = 13.2%
> > 
>