Violation in Mixing

144 Measurement of Branching Fractions for � ¦ � Ã � ¦ decays
statistical significance (in 20 fb -1 )
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7
6
5
4
3
2
1
optimization with continuum MC
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
efficiency on signal
statistical significance (in 20. fb -1 )
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7
6
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optimization with offresonance data
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
efficiency on signal
statistical significance (in 20. fb -1 )
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2
1
optimization on onresonance data
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
efficiency on signal
Figure 5-15. Result of Fisher optimization for Monte Carlo data (left), off-resonance data (center), and
on-resonance (right) data.
� � �� ¦ �� ×Ø�Ø
In the nominal fit, we used � ¦ � and in the systematics we varied the � value used of �: the fitted
value of �� ¦ �� is therefore taken into account through this systematic check. We get exactly the same
result fixing the signal components to the values from the nominal fit.
5.6 Counting analysis
As a further cross-check, a simple cut-based analysis is performed to isolate samples of events that are
consistent with the Ã Ë � and Ã Ë Ã hypotheses and signal yields are then obtained from an unbinned
maximum likelihood fit to Ñ�Ë and ¡� using the same parameterizations of the global likelihood method.
5.6.1 Cuts
A cut on the Fisher discriminant output is chosen to optimize the statistical significance. The significance as
a function of total efficiency is displayed in Fig. 5-15 for various cuts on the Fisher output. The maximum
significance is achieved with � � � . The efficiency of this cut on MC is � � with respect to the
likelihood selection.
To separate � and Ã, the SMS loose selector is applied to the tracks passing the base PID cuts described in
Sec. 5.4. Table 5-10 shows the cross-feed matrix computed from the � control sample taking into account
the proton cut and the SMS loose selection.
MARCELLA BONA