Hi Daniele,
thanks for the quick answer. This 20-30% difference was actually
the trigger for me to look more carefully to your results from the first
place. I just have difficulties to understand why a simple
(- assume statistically independent-) categorization can blow
up your fit error.
Looking at your numbers of fitted events and just adding the
the errors in quadrature I get 509+-44.3 events for the categorization
whereas you quote 559 +- 44 for no cat. . There is no 20-30% effect.
Are I am missing something (e.g. categories are not independent ..?)
Oliver
On Mon, 29 Apr 2002, Daniele del Re wrote:
>
> Hi Oliver,
>
> thanks for your good comment.
>
> > ^ ^
> > Are the two BR results | |
> > obtained from the same MC sample?
> > If yes, what has caused the shift?
> >
>
> As you see in general there is an increase of 20-30% in the error (due to
> categories with low statistics). This means that the two results can be
> different.
>
> In this particular case
>
> sqrt ( (sigma*1.3)^2 - sigma^2) ) ~ .8 sigma
>
> and 0.0179 - 0.0156 = .0023 = 1.6 * .0014(=sigma(ratio))
>
> So this result is 1.6 sigma off.
> Looking in detail
>
> * signal events from the fit w/o categories:
>
> S = 559 +- 44
>
>
> * signal events from the fit with categories:
>
> S S from truth
>
> 32 +- 7 115
> 31 +- 12 93
> 220 +- 20 662
> 190 +- 28 594
> 15 +- 15 108
> 21 +- 19 144
>
> total 509 1716
>
>
> The lack comes from the last two categories and they weight more since
> they have a small efficiency. I don't see a fitting problem in these fits
>
> http://www.slac.stanford.edu/~daniele/vub/MCmulti/newMCshapech3ne1fitresults.eps
> http://www.slac.stanford.edu/~daniele/vub/MCmulti/newMCshapech3ne2fitresults.eps
>
> BTW I will look into the problem more in detail.
>
> Thanks a lot,
>
> Daniele
>
>
>
>
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