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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}. We see 1 event in the |
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signal region (region $D$). The Standard Model MC |
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expectation is 1.4 events. |
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signal region (region $D$). For more information about |
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this one candidate events, see Appendix~\ref{sec:cand}. |
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The Standard Model MC expectation is 1.4 events. |
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\subsection{Background estimate from the ABCD method} |
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\label{sec:abcdres} |
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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 $A\times C/B$ and |
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is 1.5 $\pm$ 0.9 events (statistical uncertainty only, assuming |
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Gaussian errors), as shown in Table~\ref{tab:datayield}. |
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The prediction of the ABCD method is is given by $k_{ABCD} \times (A\times C/B)$ and |
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is $1.8 \pm 1.0(stat) \pm 0.4(syst)$ events, where $k_{ABCD} = 1.2 \pm 0.2$ as discussed |
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in Sec.~\ref{sec:abcd}. |
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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}, as well as the predicted yield in region D given |
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by A$\times$C / B. The quoted uncertainty |
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by A $\times$C / B. 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, scaled to 34.85~pb$^{-1}$.} |
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\begin{tabular}{l||c|c|c|c||c} |
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\hline |
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sample & A & B & C & D & A$\times$C / B \\ |
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sample & A & B & C & D & A $\times$ C / B \\ |
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\hline |
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$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\ |
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$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\ |
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$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\ |
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$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\ |
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$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\ |
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$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\ |
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$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\ |
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single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\ |
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$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\ |
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$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.03 & 1.47 & 0.10 & 0.10 & 0.00 \\ |
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$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\ |
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$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\ |
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$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\ |
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$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\ |
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single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\ |
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\hline |
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total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\ |
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total SM MC & 8.63 & 36.85 & 5.07 & 1.43 & 1.19 \\ |
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\hline |
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data & 11 & 36 & 5 & 1 &1.53 $\pm$ 0.86 \\ |
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data & 11 & 36 & 5 & 1 & $1.53 \pm 0.86$ \\ |
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\hline |
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\end{tabular} |
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\end{center} |
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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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\clearpage |
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%\clearpage |
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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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We first use the $P_T(\ell \ell)$ method to predict the number of events |
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in control region A, defined in Sec.~\ref{sec:abcd} as |
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$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5. |
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$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$. |
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We count the number of events in region |
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$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$ |
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cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$, |
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and find $N_{A'}=6$. To predict the yield in region A we take |
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$N_A = K \cdot K_C \cdot N_{A'} = 10.4 \pm 4.2$ |
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and find $N_{A'}=6$. We subtract off the expected DY contribution in this region |
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$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}. |
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To predict the yield in region A we take |
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$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$ |
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(statistical uncertainty only, assuming Gaussian errors), |
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where we have taken $K = 1.73$ and $K_C = 1$. This yield is in good |
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agreement with the observed yield of 11 events, as shown in |
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where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent |
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with the observed yield of 11 events, as shown in |
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Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left). |
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{\color{red} \bf Perform DY estimate for this control region}. |
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Encouraged by the good agreement between predicted and observed yields |
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in the control region, we proceed to perform the $P_T(\ell \ell)$ method |
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replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement, |
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is $N_{D'}=2$. |
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We next subtract off the expected DY contribution of |
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{\color{red} \bf $N_{DY}$ = 0.8 $\pm$ 0.8 (update DY estimate)} events, as calculated |
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$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated |
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in Sec.~\ref{sec:othBG}. The BG prediction is |
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$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 1.8^{+2.5}_{-1.8}$ (statistical |
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$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.5 \pm 2.2$ (statistical |
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uncertainty only, assuming Gaussian errors), where $K=1.54 \pm xx$ |
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as derived in Sec.~\ref{sec:victory} and $K_C = 1$. |
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This prediction is consistent with the observed yield of |
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\end{figure} |
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\begin{table}[hbt] |
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\begin{center} |
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\caption{\label{tab:victory_control}Results of the dilepton $p_{T}$ template method in the control region |
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$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for |
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$125 < \mathrm{sumJetPt} < 300$~GeV$^{1/2}$. The predicted and observed yields for |
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the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
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and MC. The error on the prediction for data is statistical only, assuming |
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Gaussian errors.} |
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\hline |
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& Predicted & Observed & Obs/Pred \\ |
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\hline |
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total SM MC & 7.10 & 8.61 & 1.21 \\ |
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data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\ |
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total SM MC & 7.18 & 8.63 & 1.20 \\ |
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data & $6.06 \pm 5.95$ & 11 & 1.82 \\ |
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\hline |
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\end{tabular} |
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\end{center} |
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\begin{table}[hbt] |
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\begin{center} |
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\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region |
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$\mathrm{sumJetPt} > 300$~GeV. The predicted and observed yields for |
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$\mathrm{sumJetPt} > 300$~GeV$^{1/2}$. The predicted and observed yields for |
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the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
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and MC. The error on the prediction for data is statistical only, assuming |
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Gaussian errors.} |
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\hline |
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& Predicted & Observed & Obs/Pred \\ |
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\hline |
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total SM MC & 0.96 & 1.41 & 1.46 \\ |
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data & $1.8^{+2.5}_{-1.8}$ & 1 & 0.56 \\ |
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total SM MC & 1.03 & 1.43 & 1.38 \\ |
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data & $2.53 \pm 2.25$ & 1 & 0.40 \\ |
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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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% \clearpage |
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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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In summary, in the signal region defined as $\mathrm{SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt} > 8.5$~GeV$^{1/2}$:\\ |
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We observe 1 event. \\ |
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We expect 1.4 events from Standard Model MC prediction. \\ |
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The ABCD data driven method predicts $1.8 \pm 1.0(stat) \pm 0.4(syst)$ events. \\ |
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The $P_T(\ell\ell)$ method predicts $2.5 \pm 2.2$ events. |
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All three background estimates are consistent within their uncertainties. |
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We thus take as our best estimate of the Standard Model yield in |
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the signal region the MC prediction and assign as an uncertainty the |
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maximal deviation with either of the data-driven methods, $N_{BG}=1.4 \pm 1.1$. |
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We conclude that 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 |
