I am performing checks of the HadrooII hadronic calibration using the
H1Lt Analysis
Framework and the code can be found in the hadronicChecks cvs package. Now you can
check too, with this
checkout script! (change the script to specify your $H1ANALYSISDIR)
All plots are made using the full high Q2 inclusive
selection (see here) and have the
HadrooII calibration applied unless otherwise stated.
The plots on this page use H1oo version 2.4.25. Go for 2.5.x plots. Or here for 2.2.13 FSCOMB High
Q2 NC sample (P.Laycock).
All of the control plots as
one file
The four main plots: pth/ptda vs ptda, pth/ptda vs gammah^e and the
same dependence for yh/yda:
Uncalibrated
Calibrated
Each set of these sets of TProfile plots shows four kinematic
balances: pth/pte, yh/yda, pth/ptda, pte/ptda in different regions of
pte: all pte, pte < 25 GeV, pte > 25 GeV and in the "golden
region" of 12 GeV < pte < 50 GeV and gammah >
15o:
TProfiles of kinematic balances as a function of gammah
TProfiles of kinematic balances as a function of ptda
TProfiles of kinematic balances as a function of pte
TProfiles of kinematic balances as a function of
gammah^e
Fits are then performed for pte/pth, pth/ptda and yh/yda across bins
in gammah, gammah^e and ptda, using 0.5*RMS and 1.1*RMS as the lower
and upper bounds respectively:
Data Pth/Pte in bins of gammah
Data Pth/Ptda in bins of gammah
Data Yh/Yda in bins of gammah
Django Pth/Pte in bins of gammah
Django Pth/Ptda in bins of
gammah
Django Yh/Yda in bins of gammah
Data Pth/Pte in bins of gammah^e
Data Pth/Ptda in bins of
gammah^e
Data Yh/Yda in bins of gammah^e
Django Pth/Pte in bins of
gammah^e
Django Pth/Ptda in bins of
gammah^e
Django Yh/Yda in bins of
gammah^e
Data Pth/Pte in bins of ptda
Data Pth/Ptda in bins of ptda
Data Yh/Yda in bins of ptda
Django Pth/Pte in bins of ptda
Django Pth/Ptda in bins of ptda
Django Yh/Yda in bins of ptda
The follwing plots show the mean of each of the fits of each of the
kinematic balances, across the bins of gammah, gammah^e and ptda. Note
that for the ptda plots the first bin is not used. Bands at 2% and 4%
of 1.0 are also shown:
Mean of Pth/Pte fit result in
bins of gammah
Mean of Pth/Ptda fit result in
bins of gammah
Mean of Yh/Yda fit result in
bins of gammah
Mean of Pth/Pte fit result in
bins of gammah^e
Mean of Pth/Ptda fit result in
bins of gammah^e
Mean of Yh/Yda fit result in
bins of gammah^e
Mean of Pth/Pte fit result in
bins of ptda
Mean of Pth/Ptda fit result in
bins of ptda
Mean of Yh/Yda fit result in
bins of ptda
Finally, these plots show the ratio of Data/MC for the above plots.
Bands at 2% and 4% of 1.0 are also shown:
(Pth/Pte)DATA/(Pth/Pte)MC in bins of gammah
(Pth/Ptda)DATA/(Pth/Ptda)MC in bins of gammah
(Yh/Yda)DATA/(Yh/Yda)MC in bins of gammah
(Pth/Pte)DATA/(Pth/Pte)MC in bins of gammah^e
(Pth/Ptda)DATA/(Pth/Ptda)MC in bins of gammah^e
(Yh/Yda)DATA/(Yh/Yda)MC in bins of gammah^e
(Pth/Pte)DATA/(Pth/Pte)MC in bins of ptda
(Pth/Ptda)DATA/(Pth/Ptda)MC in bins of ptda
(Yh/Yda)DATA/(Yh/Yda)MC in bins of ptda
Would now like to do pull distributions.....
Conclusions (so far...)
The calibration looks best at high pt and low gammah(^e).
The control plots show good agreement between data and MC; at pte
> 25 GeV the agreement is especially good.
The mean values of the Gaussian fits performed agree betwen data
and MC within 2 - 3%, but at very low values of gammah(^e) the mean
values are significantly less than 1.0 by more than 2%. This was
observed in the previous (FSCOMB) calibration - see here. This trend also occurs at low
values of ptda. The distributions also show some sinusoidal behaviour
across gammah(^e).
The Data/MC plots (derived from the fits) are mostly within 2%,
with the gammah^e distributions showing better results than the gammah
distributions. Similarly to the mean fit plots, there is a tendancy to
lower values (away from 1.0) as gammah(^e) or ptda is
decreased. Furthermore, there is a systemtic shift on the absolute
scale of about 1% (ie the average of Data/MC is about 0.99) visible
moreso in the gammah(^e) plots. This was also seen in the Marseille gammah and ptda distributions. This may be
due to iron leakage and will be investigated in the future.
However, in the full phase space (ie now not looking at the
fits), there is a definite systematic shift, where the data is always
below the MC, typically of 1 - 2% (for example in these TProfile
distributions).