| 62 |
|
events can have a significant effect on the data driven background |
| 63 |
|
prediction based on $P_T(\ell\ell)$. This is taken into account, |
| 64 |
|
based on MC expectations, |
| 65 |
< |
by the $K_{\rm{fudge}}$ factor described in that Section. |
| 65 |
> |
by the $K_C$ factor described in that Section. |
| 66 |
|
As a cross-check, we use a separate data driven method to |
| 67 |
|
estimate the impact of Drell Yan events on the |
| 68 |
|
background prediction based on $P_T(\ell\ell)$. |
| 80 |
|
outside/inside the $Z$ mass window |
| 81 |
|
in the $D'$ region. |
| 82 |
|
|
| 83 |
< |
We find $N^{D'}(ee+\mu\mu)=1$, $N^{D'}(e\mu)=0$, |
| 84 |
< |
$R^{D'}_{out/in}=0.4\pm0.3$ (stat.). Thus we estimate |
| 85 |
< |
the number of Drell Yan events in region $D'$ to |
| 86 |
< |
be $0.4\pm0.4$. |
| 83 |
> |
We find $N^{D'}(ee+\mu\mu)=2$, $N^{D'}(e\mu)=0$, |
| 84 |
> |
$R^{D'}_{out/in}=0.4\pm0.3$ (stat.). |
| 85 |
> |
{\bf Update Rout/in with dpt/pt cut and Zmumugamma veto} |
| 86 |
> |
Thus we estimate the number of Drell Yan events in region $D'$ to |
| 87 |
> |
be $0.8\pm X$ {\bf Update DY estimate}. |
| 88 |
|
|
| 89 |
|
|
| 90 |
|
This Drell Yan method could also be used to estimate |
| 154 |
|
associated with electron ID cuts applied in the trigger.} |
| 155 |
|
We then weight each event passing the full selection |
| 156 |
|
by FR/(1-FR) where FR is the ``fake rate'' for the |
| 157 |
< |
fakeable object. {\color{red} The results are...} |
| 157 |
> |
fakeable object. {\bf The results are...} |
| 158 |
|
|
| 159 |
< |
\noindent{\color{red} We will do the same thing that we did |
| 159 |
< |
for the top analysis, but we will only do it on the full dataset.} |
| 159 |
> |
{\bf We will do the same thing that we did for the top analysis, but we will only do it on the full dataset.} |
