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\section{Results} |
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\label{sec:results} |
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The data, together with SM expectations is presented |
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in Figure~\ref{fig:abcdData}. The data yields in the |
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four regions are summarized in Table~\ref{tab:datayield}. |
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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{figure} |
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The data, together with SM expectations is presented |
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in Figure~\ref{fig:abcdData}. We see $\color{red} 0$ |
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events in the signal region (region $D$). The Standard Model |
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MC expectation is {\color{red} 0.4} events. |
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|
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\subsection{Background estimate from the ABCD method} |
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\label{sec:abcdres} |
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|
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The data yields in the |
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four regions are summarized in Table~\ref{tab:datayield}. |
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The prediction of the ABCD method is is given by $AC/B$ and |
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is 0.5 events. |
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(see Table~\ref{tab:datayield}. |
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|
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\begin{table}[hbt] |
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\begin{center} |
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\caption{\label{tab:datayield} Data yields in the four |
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regions of Figure~\ref{fig:abcdData}. We also show the |
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SM Monte Carlo expectations.} |
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regions of Figure~\ref{fig:abcdData}. The quoted uncertainty |
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on the prediction in data is statistical only, assuming Gaussian errors. |
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We also show the SM Monte Carlo expectations.} |
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\begin{tabular}{|l|c|c|c|c||c|} |
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\hline |
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&A & B & C & D & AC/D \\ \hline |
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Data &3 & 6 & 1 & 0 & $0.5^{+x}_{-y}$ \\ |
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&A & B & C & D & AC/B \\ \hline |
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Data &3 & 6 & 1 & 0 & $0.5^{+0.6}_{-0.5}$ \\ |
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SM MC &2.5 &11.2 & 1.5 & 0.4 & 0.4 \\ |
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\hline |
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\end{tabular} |
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\end{center} |
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\end{table} |
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|
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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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|
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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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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 $N_{D'}=1$. Thus the BG prediction is |
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$N_D = K \cdot K_C \cdot N_{D'} = 1.5$ |
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where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory} and |
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$K_C = 1$. |
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Note that if we were to subtract off from region $D'$ |
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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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|
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%%%TO BE REPLACED |
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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 $K=1$. |
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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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|
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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 $N_{D'}=1$. 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} 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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There are |
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zero events in the signal region (region D). |
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As mentioned in Section~\ref{sec}, the number |
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of SM events expected events from Monte Carlo is 0.4. |
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The prediction of the ABCD method is 0.5 |
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(see Table~\ref{tab:datayield}. There are no events |
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in the data in region D when $P_T(\ell \ell)$ is |
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substituted for \met; thus the $P_T(\ell \ell)$ |
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method predicts a background of $0^{+x.x}_{-0.0}$ |
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events. As a cross-check, we use the $P_T(\ell \ell)$ |
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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} (We need to make sure that this prediction |
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includes the 1.4 fudge factor).} |
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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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|
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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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${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ 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 |
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quantitative limits on new physics models is discussed |
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in Section~\ref{sec:limits}. |
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in Section~\ref{sec:limit}. |
