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\section{Results} |
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\label{sec:results} |
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|
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\noindent {\color{red} In the 11 pb everything is very |
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simple because there are a few zeros. This text is written |
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for the full dataset under the assumption that some of these |
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numbers will not be zero anymore.} |
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%\noindent {\color{red} In the 11 pb everything is very |
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%simple because there are a few zeros. This text is written |
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%for the full dataset under the assumption that some of these |
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%numbers will not be zero anymore.} |
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\begin{figure}[tbh] |
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\begin{center} |
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\end{center} |
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\end{table} |
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As a cross-check, we can subtract from the yields in |
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Table~\ref{tab:datayield} the expected DY contributions |
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from Table~\ref{tab:ABCD-DY} in order to get a ``purer'' |
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estimate of the $t\bar{t}$ contribution. The result |
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of this exercise is {\color{red} xx} events. |
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%As a cross-check, we can subtract from the yields in |
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%Table~\ref{tab:datayield} the expected DY contributions |
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%from Table~\ref{tab:ABCD-DY} in order to get a ``purer'' |
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%estimate of the $t\bar{t}$ contribution. The result |
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%of this exercise is {\color{red} xx} events. |
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\subsection{Background estimate from the $P_T(\ell\ell)$ method} |
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\label{sec:victoryres} |
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{\color{red}As mentioned previously, for the 11/pb analysis |
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we use the $K$ factor from data and take $K_{\rm fudge}=1$. |
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This will change for the full dataset. We will also pay |
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more attention to the statistical errors.} |
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The number of data events in region $D'$, which is defined in |
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Section~\ref{sec:othBG} to be the same as region $D$ but with the |
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$\met/\sqrt{\rm SumJetPt}$ requirement |
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replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement |
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is $N_{D'}=0$. Thus the BG prediction is |
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$N_D = K^{MC} \cdot K_{\rm fudge} \cdot N_{D'} = xx$ |
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where we used $K^{MC}=xx$ and $K_{\rm fudge}=xx \pm yy$. |
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is $N_{D'}=1$. Thus the BG prediction is |
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$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$ |
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where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$. |
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Note that if we were to subtract off from region $D'$ |
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the {\color{red} $xx$} DY events estimated from |
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Table~\ref{tab:ABCD-DYptll}, the background |
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prediction would change to $N_D=xx$. |
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the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from |
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Section~\ref{sec:othBG}, the background |
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prediction would change to $N_D=0.9 \pm xx$ events. |
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{\color{red} When we do this with a real |
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$K_{\rm fudge}$, the fudge factor will be different |
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after the DY subtraction.} |
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As a cross-check, we use the $P_T(\ell \ell)$ |
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method to also predict the number of events in the |
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control region $120<{\rm SumJetPt}<300$ GeV and |
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\met/$\sqrt{\rm SumJetPt} > 8.5$. We predict |
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$5.6^{+x}_{-y}$ events and we observe 4. |
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{\color{red} Note: when we do this more carefully |
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we will need to use a different $K$ and a different $K_{fudge}$>} |
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The results of the $P_T(\ell\ell)$ method are |
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summarized in Figure~\ref{fig:victory}. |
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\begin{figure}[hbt] |
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\begin{center} |
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\includegraphics[width=0.48\linewidth]{victory_control.png} |
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\includegraphics[width=0.48\linewidth]{victory_sig.png} |
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\caption{\label{fig:victory}\protect Distributions of |
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tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region. |
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We show the oberved distributions in both Monte Carlo and data. |
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We also show the distributions predicted from |
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tcMet/$\sqrt{P_T(\ell\ell)}$ in both MC and data.} |
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\end{center} |
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\end{figure} |
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\subsection{Summary of results} |
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To summarize: we see no evidence for an anomalous |
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rate of opposite sign isolated dilepton events |
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at high \met and high SumJetPt. The extraction of |
