Hi,
on the web page where I posted results of systematics due to
randomization of S/P:
http://www.slac.stanford.edu/~petrella/systsp.html
you can see that this systematic error is not stable, for example when
cutting on integrated purtity.
Now I'm trying to look at other results from these jobs to see if I can
find what makes this errors so large, but probably this is also due to
the S/P ratio and its error.
For example if you look at the correction factors for IP > 0.50
(http://www.slac.stanford.edu/~petrella/tmp/SP_allrew/SPallweights/ip050_allrew/corrallwip050pol1.eps)
you can see that the first bin has a large error (the exact value of the
correction factor for this bin is S/P = 5.67 +- 5.34)
These numbers (they're on the spreadsheet at
http://www.slac.stanford.edu/~petrella/tmp/SP_allrew/SPallweights/SoverPFullRew.sxc)
come from the double ratio of S/P on MC (0.74 +- 0.13) times the S/P
ratio on data depleted sample
(http://www.slac.stanford.edu/~petrella/tmp/SP_allrew/SPallweights/ip050_allrew/data_depl_AC_intp0.50_0.001.55.eps)
On data depleted sample the signal component (fitted) is 291 +- 31 and
the background component (fitted) is 38 +- 35, so the error on the final
S/P ratio is driven by the background component on data depleted
sample... and cutting on purity (and having less background) will give
roughly higher errors on background component (at least the statistical
error).
For the data depleted sample we get S +- dS and P +- dP as they come out
from the fit and then we compute the quantity S/P +- d(S/P). But these
errors are correlated, aren't they?
ciao,
Antonio
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