| 1 |
benhoob |
1.7 |
\clearpage
|
| 2 |
|
|
|
| 3 |
claudioc |
1.1 |
\section{Results}
|
| 4 |
|
|
\label{sec:results}
|
| 5 |
|
|
|
| 6 |
|
|
\begin{figure}[tbh]
|
| 7 |
|
|
\begin{center}
|
| 8 |
benhoob |
1.7 |
\includegraphics[width=0.75\linewidth]{abcd_35pb.png}
|
| 9 |
claudioc |
1.1 |
\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
|
| 10 |
|
|
vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data. Here we also
|
| 11 |
|
|
show our choice of ABCD regions.}
|
| 12 |
|
|
\end{center}
|
| 13 |
|
|
\end{figure}
|
| 14 |
|
|
|
| 15 |
claudioc |
1.2 |
The data, together with SM expectations is presented
|
| 16 |
benhoob |
1.7 |
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.
|
| 19 |
claudioc |
1.2 |
|
| 20 |
|
|
\subsection{Background estimate from the ABCD method}
|
| 21 |
|
|
\label{sec:abcdres}
|
| 22 |
|
|
|
| 23 |
|
|
The data yields in the
|
| 24 |
|
|
four regions are summarized in Table~\ref{tab:datayield}.
|
| 25 |
benhoob |
1.7 |
The prediction of the ABCD method is is given by $A\times C/B$ and
|
| 26 |
|
|
is 1.5 events. (see Table~\ref{tab:datayield}.
|
| 27 |
claudioc |
1.2 |
|
| 28 |
claudioc |
1.1 |
\begin{table}[hbt]
|
| 29 |
|
|
\begin{center}
|
| 30 |
|
|
\caption{\label{tab:datayield} Data yields in the four
|
| 31 |
benhoob |
1.7 |
regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
|
| 32 |
|
|
by A$\times$C / B. The quoted uncertainty
|
| 33 |
benhoob |
1.4 |
on the prediction in data is statistical only, assuming Gaussian errors.
|
| 34 |
benhoob |
1.7 |
We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.}
|
| 35 |
|
|
\begin{tabular}{l||c|c|c|c||c}
|
| 36 |
|
|
\hline
|
| 37 |
|
|
sample & A & B & C & D & A$\times$C / B \\
|
| 38 |
|
|
\hline
|
| 39 |
|
|
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\
|
| 40 |
|
|
$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\
|
| 41 |
|
|
$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\
|
| 42 |
|
|
$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\
|
| 43 |
|
|
$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\
|
| 44 |
|
|
$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\
|
| 45 |
|
|
$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\
|
| 46 |
|
|
single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\
|
| 47 |
|
|
\hline
|
| 48 |
|
|
total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\
|
| 49 |
claudioc |
1.1 |
\hline
|
| 50 |
benhoob |
1.7 |
data & 11 & 36 & 5 & 1 &1.53 $\pm$ 0.86 \\
|
| 51 |
claudioc |
1.1 |
\hline
|
| 52 |
|
|
\end{tabular}
|
| 53 |
|
|
\end{center}
|
| 54 |
|
|
\end{table}
|
| 55 |
|
|
|
| 56 |
claudioc |
1.3 |
%As a cross-check, we can subtract from the yields in
|
| 57 |
|
|
%Table~\ref{tab:datayield} the expected DY contributions
|
| 58 |
|
|
%from Table~\ref{tab:ABCD-DY} in order to get a ``purer''
|
| 59 |
|
|
%estimate of the $t\bar{t}$ contribution. The result
|
| 60 |
|
|
%of this exercise is {\color{red} xx} events.
|
| 61 |
claudioc |
1.2 |
|
| 62 |
|
|
\subsection{Background estimate from the $P_T(\ell\ell)$ method}
|
| 63 |
|
|
\label{sec:victoryres}
|
| 64 |
|
|
|
| 65 |
|
|
The number of data events in region $D'$, which is defined in
|
| 66 |
|
|
Section~\ref{sec:othBG} to be the same as region $D$ but with the
|
| 67 |
|
|
$\met/\sqrt{\rm SumJetPt}$ requirement
|
| 68 |
|
|
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
|
| 69 |
benhoob |
1.8 |
is $N_{D'}=2$. Thus the BG prediction is
|
| 70 |
benhoob |
1.6 |
$N_D = K \cdot K_C \cdot N_{D'} = 1.5$
|
| 71 |
|
|
where $K=1.5 \pm xx$ as derived in Sec.~\ref{sec:victory} and
|
| 72 |
|
|
$K_C = 1$.
|
| 73 |
claudioc |
1.2 |
Note that if we were to subtract off from region $D'$
|
| 74 |
benhoob |
1.8 |
the {\color{red} 0.8 $\pm$ 0.8} DY events estimated from
|
| 75 |
claudioc |
1.3 |
Section~\ref{sec:othBG}, the background
|
| 76 |
benhoob |
1.8 |
prediction would change to $N_D=1.8 \pm xx$ events.
|
| 77 |
benhoob |
1.4 |
|
| 78 |
|
|
%%%TO BE REPLACED
|
| 79 |
|
|
%{\color{red}As mentioned previously, for the 11/pb analysis
|
| 80 |
|
|
%we use the $K$ factor from data and take $K=1$.
|
| 81 |
|
|
%This will change for the full dataset. We will also pay
|
| 82 |
|
|
%more attention to the statistical errors.}
|
| 83 |
|
|
|
| 84 |
|
|
%The number of data events in region $D'$, which is defined in
|
| 85 |
|
|
%Section~\ref{sec:othBG} to be the same as region $D$ but with the
|
| 86 |
|
|
%$\met/\sqrt{\rm SumJetPt}$ requirement
|
| 87 |
|
|
%replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement
|
| 88 |
|
|
%is $N_{D'}=1$. Thus the BG prediction is
|
| 89 |
|
|
%$N_D = K \cdot K_{\rm fudge} \cdot N_{D'} = 1.5$
|
| 90 |
|
|
%where we used $K=1.5 \pm xx$ and $K_{\rm fudge}=1.0 \pm 0.0$.
|
| 91 |
|
|
%Note that if we were to subtract off from region $D'$
|
| 92 |
|
|
%the {\color{red} 0.4 $\pm$ 0.4} DY events estimated from
|
| 93 |
|
|
%Section~\ref{sec:othBG}, the background
|
| 94 |
|
|
%prediction would change to $N_D=0.9 \pm xx$ events.
|
| 95 |
|
|
%{\color{red} When we do this with a real
|
| 96 |
|
|
%$K_{\rm fudge}$, the fudge factor will be different
|
| 97 |
|
|
%after the DY subtraction.}
|
| 98 |
claudioc |
1.1 |
|
| 99 |
claudioc |
1.2 |
As a cross-check, we use the $P_T(\ell \ell)$
|
| 100 |
claudioc |
1.1 |
method to also predict the number of events in the
|
| 101 |
benhoob |
1.8 |
control region $125<{\rm SumJetPt}<300$ GeV and
|
| 102 |
claudioc |
1.1 |
\met/$\sqrt{\rm SumJetPt} > 8.5$. We predict
|
| 103 |
|
|
$5.6^{+x}_{-y}$ events and we observe 4.
|
| 104 |
claudioc |
1.3 |
The results of the $P_T(\ell\ell)$ method are
|
| 105 |
|
|
summarized in Figure~\ref{fig:victory}.
|
| 106 |
claudioc |
1.1 |
|
| 107 |
claudioc |
1.3 |
\begin{figure}[hbt]
|
| 108 |
|
|
\begin{center}
|
| 109 |
benhoob |
1.8 |
\includegraphics[width=0.48\linewidth]{victory_control_35pb.png}
|
| 110 |
|
|
\includegraphics[width=0.48\linewidth]{victory_signal_35pb.png}
|
| 111 |
claudioc |
1.3 |
\caption{\label{fig:victory}\protect Distributions of
|
| 112 |
|
|
tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
|
| 113 |
|
|
We show the oberved distributions in both Monte Carlo and data.
|
| 114 |
|
|
We also show the distributions predicted from
|
| 115 |
benhoob |
1.4 |
${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
|
| 116 |
claudioc |
1.3 |
\end{center}
|
| 117 |
|
|
\end{figure}
|
| 118 |
|
|
|
| 119 |
|
|
|
| 120 |
|
|
\subsection{Summary of results}
|
| 121 |
claudioc |
1.1 |
To summarize: we see no evidence for an anomalous
|
| 122 |
|
|
rate of opposite sign isolated dilepton events
|
| 123 |
|
|
at high \met and high SumJetPt. The extraction of
|
| 124 |
|
|
quantitative limits on new physics models is discussed
|
| 125 |
benhoob |
1.5 |
in Section~\ref{sec:limit}. |
