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claudioc |
1.22 |
%\clearpage
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benhoob |
1.7 |
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claudioc |
1.1 |
\section{Results}
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\label{sec:results}
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\begin{figure}[tbh]
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\begin{center}
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1.29 |
\includegraphics[width=0.75\linewidth]{abcd_38XMC.png}
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claudioc |
1.1 |
\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
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benhoob |
1.29 |
vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data.}
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claudioc |
1.1 |
\end{center}
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\end{figure}
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claudioc |
1.2 |
The data, together with SM expectations is presented
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1.7 |
in Figure~\ref{fig:abcdData}. We see 1 event in the
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claudioc |
1.17 |
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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1.29 |
The Standard Model MC expectation is 1.3 events.
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claudioc |
1.2 |
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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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benhoob |
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The prediction of the ABCD method is is given by $A \times C / B = 1.5 \pm 0.9({\rm stat}) \pm 0.3({\rm syst})$.
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claudioc |
1.2 |
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claudioc |
1.1 |
\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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benhoob |
1.7 |
regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
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benhoob |
1.29 |
by A $\times$ C / B. The quoted uncertainty
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benhoob |
1.4 |
on the prediction in data is statistical only, assuming Gaussian errors.
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benhoob |
1.7 |
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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1.26 |
sample & A & B & C & D & A $\times$ C / B \\
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1.24 |
\hline
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1.26 |
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 8.27 $\pm$ 0.18 & 32.16 $\pm$ 0.35 & 4.69 $\pm$ 0.13 & 1.05 $\pm$ 0.06 & 1.21 $\pm$ 0.04 \\
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$t\bar{t}\rightarrow \mathrm{other}$ & 0.12 $\pm$ 0.02 & 0.77 $\pm$ 0.05 & 0.15 $\pm$ 0.02 & 0.02 $\pm$ 0.01 & 0.02 $\pm$ 0.01 \\
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$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.22 $\pm$ 0.11 & 1.54 $\pm$ 0.29 & 0.05 $\pm$ 0.05 & 0.16 $\pm$ 0.09 & 0.01 $\pm$ 0.01 \\
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$W^{\pm}$ + jets & 0.00 $\pm$ 0.00 & 0.10 $\pm$ 0.10 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
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1.26 |
$W^+W^-$ & 0.11 $\pm$ 0.01 & 0.30 $\pm$ 0.02 & 0.02 $\pm$ 0.01 & 0.03 $\pm$ 0.01 & 0.01 $\pm$ 0.00 \\
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$W^{\pm}Z^0$ & 0.01 $\pm$ 0.00 & 0.04 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
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$Z^0Z^0$ & 0.01 $\pm$ 0.00 & 0.02 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
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single top & 0.30 $\pm$ 0.01 & 1.06 $\pm$ 0.03 & 0.04 $\pm$ 0.01 & 0.01 $\pm$ 0.00 & 0.01 $\pm$ 0.00 \\
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benhoob |
1.7 |
\hline
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1.26 |
total SM MC & 9.03 $\pm$ 0.21 & 35.97 $\pm$ 0.46 & 4.97 $\pm$ 0.15 & 1.29 $\pm$ 0.11 & 1.25 $\pm$ 0.05 \\
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claudioc |
1.1 |
\hline
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1.26 |
data & 11 & 36 & 5 & 1 & 1.53 $\pm$ 0.86 \\
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claudioc |
1.1 |
\hline
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\end{tabular}
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\end{center}
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\end{table}
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claudioc |
1.3 |
%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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claudioc |
1.2 |
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claudioc |
1.22 |
%\clearpage
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benhoob |
1.10 |
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claudioc |
1.2 |
\subsection{Background estimate from the $P_T(\ell\ell)$ method}
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\label{sec:victoryres}
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benhoob |
1.11 |
We first use the $P_T(\ell \ell)$ method to predict the number of events
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benhoob |
1.12 |
in control region A, defined in Sec.~\ref{sec:abcd} as
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benhoob |
1.19 |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$.
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benhoob |
1.12 |
We count the number of events in region
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$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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benhoob |
1.15 |
and find $N_{A'}=6$. We subtract off the expected DY contribution in this region
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benhoob |
1.29 |
$N_{DY} = 1.65 \pm 1.13$, derived in Sec.~\ref{sec:othBG}.
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benhoob |
1.15 |
To predict the yield in region A we take
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benhoob |
1.27 |
$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 10.7 \pm 6.6$
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where we have taken $K = 1.73$ and $K_C = 1.4$.
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This uncertainty takes into account the statistical uncertainties in $N_{A'}$ and $N_{DY}$,
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assuming Gaussian errors.
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This yield is consistent
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1.15 |
with the observed yield of 11 events, as shown in
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1.29 |
Table~\ref{tab:victory} and displayed in Fig.~\ref{fig:victory} (left).
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benhoob |
1.11 |
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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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in the signal region ${\rm SumJetPt}>300$~GeV.
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claudioc |
1.2 |
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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benhoob |
1.11 |
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement,
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benhoob |
1.13 |
is $N_{D'}=2$.
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benhoob |
1.27 |
%We next subtract off the expected DY contribution of
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%$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated
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%in Sec.~\ref{sec:othBG}.
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The BG prediction is
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$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 4.3 \pm 3.0({\rm stat}) \pm 1.2({\rm syst})$
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benhoob |
1.29 |
where $K=1.52$ as derived in Sec.~\ref{sec:victory} and $K_C = 1.4 \pm 0.4$.
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benhoob |
1.13 |
This prediction is consistent with the observed yield of
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benhoob |
1.29 |
1 event, as summarized in Table~\ref{tab:victory} and Fig.~\ref{fig:victory}
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benhoob |
1.12 |
(right).
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benhoob |
1.11 |
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claudioc |
1.1 |
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claudioc |
1.3 |
\begin{figure}[hbt]
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\begin{center}
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1.30 |
\includegraphics[width=0.48\linewidth]{victory_control_38XMC.png}
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\includegraphics[width=0.48\linewidth]{victory_signal_38XMC.png}
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claudioc |
1.3 |
\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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benhoob |
1.4 |
${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
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claudioc |
1.3 |
\end{center}
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\end{figure}
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benhoob |
1.9 |
\begin{table}[hbt]
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\begin{center}
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benhoob |
1.27 |
\caption{\label{tab:victory}Results of the dilepton $p_{T}$ template method in the control region
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($125 < \mathrm{SumJetPt} < 300$~GeV) and signal region ($\mathrm{SumJetPt} > 300$~GeV). The predicted and
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observed yields for the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}} > 8.5$~GeV$^{1/2}$. The errors are
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1.31 |
statistical only, assuming Gaussian errors. Note that the correction factor $K_C = 1.4$ has been applied
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to the data but not to the MC.}
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\begin{tabular}{l|cc|cc}
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1.27 |
\hline
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& Control Region & & Signal Region & \\
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1.9 |
\hline
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benhoob |
1.27 |
& Predicted & Observed & Predicted & Observed \\
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benhoob |
1.9 |
\hline
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benhoob |
1.27 |
total SM MC & 6.36 & 9.03 & 0.92 & 1.29 \\
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data & $10.7 \pm 6.6$ & 11 & $4.3 \pm 3.0$ & 1 \\
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1.9 |
\hline
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\end{tabular}
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\end{center}
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\end{table}
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claudioc |
1.22 |
% \clearpage
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claudioc |
1.3 |
\subsection{Summary of results}
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benhoob |
1.21 |
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benhoob |
1.28 |
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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benhoob |
1.21 |
We observe 1 event. \\
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benhoob |
1.28 |
We expect 1.3 events from Standard Model MC prediction. \\
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benhoob |
1.27 |
The ABCD data driven method predicts $1.5 \pm 0.9({\rm stat}) \pm 0.3({\rm syst})$ events. \\
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The $P_T(\ell\ell)$ method predicts $4.3 \pm 3.0({\rm stat}) \pm 1.2({\rm syst})$ events.
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benhoob |
1.21 |
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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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benhoob |
1.30 |
the signal region the average of the predicted yields from the 2 data-driven methods, weighted by their uncertainties.
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This procedure gives an expected background yield $N_{BG}=1.7 \pm 1.1$.
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benhoob |
1.21 |
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We conclude that we see no evidence for an anomalous
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claudioc |
1.1 |
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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1.5 |
in Section~\ref{sec:limit}. |
