| 1 |
< |
\clearpage |
| 1 |
> |
%\clearpage |
| 2 |
|
|
| 3 |
|
\section{Results} |
| 4 |
|
\label{sec:results} |
| 14 |
|
|
| 15 |
|
The data, together with SM expectations is presented |
| 16 |
|
in Figure~\ref{fig:abcdData}. We see 1 event in the |
| 17 |
< |
signal region (region $D$). The Standard Model MC |
| 18 |
< |
expectation is 1.4 events. |
| 17 |
> |
signal region (region $D$). For more information about |
| 18 |
> |
this one candidate events, see Appendix~\ref{sec:cand}. |
| 19 |
> |
The Standard Model MC expectation is 1.4 events. |
| 20 |
|
|
| 21 |
|
\subsection{Background estimate from the ABCD method} |
| 22 |
|
\label{sec:abcdres} |
| 23 |
|
|
| 24 |
|
The data yields in the |
| 25 |
|
four regions are summarized in Table~\ref{tab:datayield}. |
| 26 |
< |
The prediction of the ABCD method is is given by $A\times C/B$ and |
| 27 |
< |
is 1.5 $\pm$ 0.9 events (statistical uncertainty only, assuming |
| 28 |
< |
Gaussian errors), as shown in Table~\ref{tab:datayield}. |
| 26 |
> |
The prediction of the ABCD method is is given by $k_{ABCD} \times (A\times C/B)$ and |
| 27 |
> |
is $1.8 \pm 1.0(stat) \pm 0.4(syst)$ events, where $k_{ABCD} = 1.2 \pm 0.2$ as discussed |
| 28 |
> |
in Sec.~\ref{sec:abcd}. |
| 29 |
|
|
| 30 |
|
\begin{table}[hbt] |
| 31 |
|
\begin{center} |
| 50 |
|
\hline |
| 51 |
|
total SM MC & 8.63 & 36.85 & 5.07 & 1.43 & 1.19 \\ |
| 52 |
|
\hline |
| 53 |
< |
data & 11 & 36 & 5 & 1 & $1.53\pm0.86$ \\ |
| 53 |
> |
data & 11 & 36 & 5 & 1 & $1.53 \pm 0.86$ \\ |
| 54 |
|
\hline |
| 55 |
|
\end{tabular} |
| 56 |
|
\end{center} |
| 62 |
|
%estimate of the $t\bar{t}$ contribution. The result |
| 63 |
|
%of this exercise is {\color{red} xx} events. |
| 64 |
|
|
| 65 |
< |
\clearpage |
| 65 |
> |
%\clearpage |
| 66 |
|
|
| 67 |
|
\subsection{Background estimate from the $P_T(\ell\ell)$ method} |
| 68 |
|
\label{sec:victoryres} |
| 69 |
|
|
| 70 |
|
We first use the $P_T(\ell \ell)$ method to predict the number of events |
| 71 |
|
in control region A, defined in Sec.~\ref{sec:abcd} as |
| 72 |
< |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5. |
| 72 |
> |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$. |
| 73 |
|
We count the number of events in region |
| 74 |
|
$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$ |
| 75 |
|
cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$, |
| 76 |
< |
and find $N_{A'}=6$. To predict the yield in region A we take |
| 77 |
< |
$N_A = K \cdot K_C \cdot N_{A'} = 10.4 \pm 4.2$ |
| 76 |
> |
and find $N_{A'}=6$. We subtract off the expected DY contribution in this region |
| 77 |
> |
$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}. |
| 78 |
> |
To predict the yield in region A we take |
| 79 |
> |
$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$ |
| 80 |
|
(statistical uncertainty only, assuming Gaussian errors), |
| 81 |
< |
where we have taken $K = 1.73$ and $K_C = 1$. This yield is in good |
| 82 |
< |
agreement with the observed yield of 11 events, as shown in |
| 81 |
> |
where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent |
| 82 |
> |
with the observed yield of 11 events, as shown in |
| 83 |
|
Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left). |
| 84 |
|
|
| 85 |
|
Encouraged by the good agreement between predicted and observed yields |
| 118 |
|
\begin{table}[hbt] |
| 119 |
|
\begin{center} |
| 120 |
|
\caption{\label{tab:victory_control}Results of the dilepton $p_{T}$ template method in the control region |
| 121 |
< |
$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for |
| 121 |
> |
$125 < \mathrm{sumJetPt} < 300$~GeV$^{1/2}$. The predicted and observed yields for |
| 122 |
|
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
| 123 |
|
and MC. The error on the prediction for data is statistical only, assuming |
| 124 |
|
Gaussian errors.} |
| 127 |
|
& Predicted & Observed & Obs/Pred \\ |
| 128 |
|
\hline |
| 129 |
|
total SM MC & 7.18 & 8.63 & 1.20 \\ |
| 130 |
< |
data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\ |
| 130 |
> |
data & $6.06 \pm 5.95$ & 11 & 1.82 \\ |
| 131 |
|
\hline |
| 132 |
|
\end{tabular} |
| 133 |
|
\end{center} |
| 136 |
|
\begin{table}[hbt] |
| 137 |
|
\begin{center} |
| 138 |
|
\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region |
| 139 |
< |
$\mathrm{sumJetPt} > 300$~GeV. The predicted and observed yields for |
| 139 |
> |
$\mathrm{sumJetPt} > 300$~GeV$^{1/2}$. The predicted and observed yields for |
| 140 |
|
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
| 141 |
|
and MC. The error on the prediction for data is statistical only, assuming |
| 142 |
|
Gaussian errors.} |
| 152 |
|
\end{table} |
| 153 |
|
|
| 154 |
|
|
| 155 |
+ |
% \clearpage |
| 156 |
|
\subsection{Summary of results} |
| 157 |
< |
To summarize: we see no evidence for an anomalous |
| 157 |
> |
|
| 158 |
> |
In summary, in the signal region defined as $\mathrm{SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt} > 8.5$~GeV$^{1/2}$:\\ |
| 159 |
> |
We observe 1 event. \\ |
| 160 |
> |
We expect 1.4 events from Standard Model MC prediction. \\ |
| 161 |
> |
The ABCD data driven method predicts $1.8 \pm 1.0(stat) \pm 0.4(syst)$ events. \\ |
| 162 |
> |
The $P_T(\ell\ell)$ method predicts $2.5 \pm 2.2$ events. |
| 163 |
> |
|
| 164 |
> |
All three background estimates are consistent within their uncertainties. |
| 165 |
> |
We thus take as our best estimate of the Standard Model yield in |
| 166 |
> |
the signal region the MC prediction and assign as an uncertainty the |
| 167 |
> |
maximal deviation with either of the data-driven methods, $N_{BG}=1.4 \pm 1.1$. |
| 168 |
> |
|
| 169 |
> |
We conclude that we see no evidence for an anomalous |
| 170 |
|
rate of opposite sign isolated dilepton events |
| 171 |
|
at high \met and high SumJetPt. The extraction of |
| 172 |
|
quantitative limits on new physics models is discussed |
