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1.1 |
\section{Results}
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\label{sec:results}
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1.3 |
%\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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claudioc |
1.1 |
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\begin{figure}[tbh]
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\begin{center}
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\includegraphics[width=0.75\linewidth]{abcdData.png}
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\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
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vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data. Here we also
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show our choice of ABCD regions.}
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\end{center}
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\end{figure}
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1.2 |
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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\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 $AC/B$ and
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is 0.5 events.
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(see Table~\ref{tab:datayield}.
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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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regions of Figure~\ref{fig:abcdData}. We also show the
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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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claudioc |
1.2 |
&A & B & C & D & AC/B \\ \hline
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claudioc |
1.1 |
Data &3 & 6 & 1 & 0 & $0.5^{+x}_{-y}$ \\
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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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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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\subsection{Background estimate from the $P_T(\ell\ell)$ method}
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\label{sec:victoryres}
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claudioc |
1.3 |
{\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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claudioc |
1.2 |
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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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claudioc |
1.2 |
Note that if we were to subtract off from region $D'$
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claudioc |
1.3 |
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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claudioc |
1.1 |
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claudioc |
1.2 |
As a cross-check, we use the $P_T(\ell \ell)$
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claudioc |
1.1 |
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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claudioc |
1.3 |
The results of the $P_T(\ell\ell)$ method are
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summarized in Figure~\ref{fig:victory}.
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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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\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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claudioc |
1.1 |
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}. |
