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Ok, so why do you get 559+-44 w/o cat. and 509+-44 with cat.? Or more detailed: a) What happened to the other 50 events? b) Why do you get the same error for 509 and 559 events? On Mon, 29 Apr 2002, Daniele del Re wrote: > > Hi Oliver, > > > Now I am really confused. > > > > You are trying to make me believe that the total number of > > measured events dependence on the categorization. Well, I > > always thought that adding up the individual cat. should yield > > the same number of events than making no categorization. > > Apparently thats not the case ... why? If everything > > is self consistent it should ... isn't it. > > At this stage we do not have to care about eff. corrections. > > I don't want to make you believe anything, I am just saying that > > 1) I calcute how many events I have per category > 2) I apply the efficiency per category to those numbers > 3) I put together the numbers > > this is different from putting them together before and then dividing by > the overall efficiency. The final number and the final error > come out different (as I showed in the simple example in the previous > mail). > > Cheers, > > Daniele > > > > > > > > > Oliver > > > > By the way, > > > > even in your simple example the total number of measured > > events before eff. correction has to be the same. If you would have > > a 10% discrepancy there; at least one of the two results (with or w/o > > cat.) has to be wrong. > > > > > > On Mon, 29 Apr 2002, Daniele del Re wrote: > > > > > > > > Hi, > > > > > > > Thanks Daniele, I will think about this eff. stuff a bit more. > > > > However, in my last mail I was indicating a much more basic item only related to > > > > the measurement of number of events. Your measurement is: > > > > > > > > 559+-44 w/o cat. > > > > 509+-44 with cat. > > > > > > > > => same error but roughly 10% different yield (correct?) > > > > > > yes, this 10% less explains the difference but this effect will be > > > enhanced once you apply the efficiency per category. > > > You must get the same error if you put together the number before the > > > efficiency correction. > > > > > > > > > > > Even in your example > > > > below you assume the same number of measured events ..isn't it. > > > > This 10% might explain the difference between old and new results. > > > > > > > > The eff. stuff is the second step after you have already performed > > > > the measurement. Hence not effecting your fit results and fit errors > > > > (correct?!) > > > > > > This is not correct. Forget about 509 +- 44. You have to put together all > > > numbers only after you will divide by each efficiency. > > > > > > Daniele > > > > > > > > > > > Did you see my point? > > > > > > > > > > > > Oliver > > > > > > > > On Mon, 29 Apr 2002, Daniele del Re wrote: > > > > > > > > > > > > > > Hi, > > > > > > > > > > each category has a different efficiency. If you correct by the > > > > > efficiency before putting together the results you will get a larger > > > > > error. For instance: > > > > > > > > > > suppose to have just two categories > > > > > > > > > > eff(1) = 90% > > > > > eff(2) = 10% > > > > > > > > > > while > > > > > > > > > > eff(overall) = 50% (same amount of events in both categories at the > > > > > origin) > > > > > > > > > > Suppose to measure > > > > > > > > > > N(1) = 900 +- 30 > > > > > N(2) = 100 +- 10 => > > > > > > > > > > Then > > > > > > > > > > N(1)_origin = 1000 +- 33 > > > > > N(2)_origin = 1000 +- 100 => Ntot_origin = 2000 +- 105 > > > > > > > > > > > > > > > while using just one category and one efficiency you get > > > > > > > > > > N = 1000 +- 32 => (eff = 50%) > > > > > > > > > > Ntot_origin = 2000 +- 64 > > > > > > > > > > > > > > > The effect depends on the difference in the efficiencies and on the the > > > > > number of events in each category. > > > > > > > > > > > > > > > Since in our categorization we have two "bad" categories (the last two, > > > > > ch3ne1 and ch3ne2) with small efficiencies and containing a pretty > > > > > large fraction of events at the origin, the final result can have a much > > > > > different error. > > > > > > > > > > > > > > > Daniele > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > On Mon, 29 Apr 2002, Oliver Buchmueller wrote: > > > > > > > > > > > > > > > > > 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 > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >