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\begin{table}[hbt] |
| 34 |
|
\begin{center} |
| 35 |
|
\caption{\label{tab:datayield} Data yields in the four |
| 36 |
< |
regions of Figure~\ref{fig:abcdData}. We also show the |
| 37 |
< |
SM Monte Carlo expectations.} |
| 36 |
> |
regions of Figure~\ref{fig:abcdData}. The quoted uncertainty |
| 37 |
> |
on the prediction in data is statistical only, assuming Gaussian errors. |
| 38 |
> |
We also show the SM Monte Carlo expectations.} |
| 39 |
|
\begin{tabular}{|l|c|c|c|c||c|} |
| 40 |
|
\hline |
| 41 |
|
&A & B & C & D & AC/B \\ \hline |
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< |
Data &3 & 6 & 1 & 0 & $0.5^{+x}_{-y}$ \\ |
| 42 |
> |
Data &3 & 6 & 1 & 0 & $0.5^{+0.6}_{-0.5}$ \\ |
| 43 |
|
SM MC &2.5 &11.2 & 1.5 & 0.4 & 0.4 \\ |
| 44 |
|
\hline |
| 45 |
|
\end{tabular} |
| 55 |
|
\subsection{Background estimate from the $P_T(\ell\ell)$ method} |
| 56 |
|
\label{sec:victoryres} |
| 57 |
|
|
| 57 |
– |
|
| 58 |
– |
{\color{red}As mentioned previously, for the 11/pb analysis |
| 59 |
– |
we use the $K$ factor from data and take $K_{\rm fudge}=1$. |
| 60 |
– |
This will change for the full dataset. We will also pay |
| 61 |
– |
more attention to the statistical errors.} |
| 62 |
– |
|
| 58 |
|
The number of data events in region $D'$, which is defined in |
| 59 |
|
Section~\ref{sec:othBG} to be the same as region $D$ but with the |
| 60 |
|
$\met/\sqrt{\rm SumJetPt}$ requirement |
| 61 |
|
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement |
| 62 |
|
is $N_{D'}=1$. Thus the BG prediction is |
| 63 |
< |
$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$ |
| 64 |
< |
where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$. |
| 63 |
> |
$N_D = K \cdot N_{D'} = 1.5$ |
| 64 |
> |
where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory}. |
| 65 |
|
Note that if we were to subtract off from region $D'$ |
| 66 |
|
the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from |
| 67 |
|
Section~\ref{sec:othBG}, the background |
| 68 |
|
prediction would change to $N_D=0.9 \pm xx$ events. |
| 69 |
< |
{\color{red} When we do this with a real |
| 70 |
< |
$K_{\rm fudge}$, the fudge factor will be different |
| 71 |
< |
after the DY subtraction.} |
| 69 |
> |
|
| 70 |
> |
%%%TO BE REPLACED |
| 71 |
> |
%{\color{red}As mentioned previously, for the 11/pb analysis |
| 72 |
> |
%we use the $K$ factor from data and take $K=1$. |
| 73 |
> |
%This will change for the full dataset. We will also pay |
| 74 |
> |
%more attention to the statistical errors.} |
| 75 |
> |
|
| 76 |
> |
%The number of data events in region $D'$, which is defined in |
| 77 |
> |
%Section~\ref{sec:othBG} to be the same as region $D$ but with the |
| 78 |
> |
%$\met/\sqrt{\rm SumJetPt}$ requirement |
| 79 |
> |
%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement |
| 80 |
> |
%is $N_{D'}=1$. Thus the BG prediction is |
| 81 |
> |
%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$ |
| 82 |
> |
%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$. |
| 83 |
> |
%Note that if we were to subtract off from region $D'$ |
| 84 |
> |
%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from |
| 85 |
> |
%Section~\ref{sec:othBG}, the background |
| 86 |
> |
%prediction would change to $N_D=0.9 \pm xx$ events. |
| 87 |
> |
%{\color{red} When we do this with a real |
| 88 |
> |
%$K_{\rm fudge}$, the fudge factor will be different |
| 89 |
> |
%after the DY subtraction.} |
| 90 |
|
|
| 91 |
|
As a cross-check, we use the $P_T(\ell \ell)$ |
| 92 |
|
method to also predict the number of events in the |
| 104 |
|
tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region. |
| 105 |
|
We show the oberved distributions in both Monte Carlo and data. |
| 106 |
|
We also show the distributions predicted from |
| 107 |
< |
tcMet/$\sqrt{P_T(\ell\ell)}$ in both MC and data.} |
| 107 |
> |
${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.} |
| 108 |
|
\end{center} |
| 109 |
|
\end{figure} |
| 110 |
|
|
