| 1 |
claudioc |
1.22 |
%\clearpage
|
| 2 |
benhoob |
1.7 |
|
| 3 |
claudioc |
1.1 |
\section{Results}
|
| 4 |
|
|
\label{sec:results}
|
| 5 |
|
|
|
| 6 |
|
|
\begin{figure}[tbh]
|
| 7 |
|
|
\begin{center}
|
| 8 |
benhoob |
1.32 |
\includegraphics[width=0.75\linewidth]{abcd_v3.png}
|
| 9 |
claudioc |
1.1 |
\caption{\label{fig:abcdData}\protect Distributions of SumJetPt
|
| 10 |
benhoob |
1.29 |
vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo and data.}
|
| 11 |
claudioc |
1.1 |
\end{center}
|
| 12 |
|
|
\end{figure}
|
| 13 |
|
|
|
| 14 |
claudioc |
1.2 |
The data, together with SM expectations is presented
|
| 15 |
benhoob |
1.7 |
in Figure~\ref{fig:abcdData}. We see 1 event in the
|
| 16 |
claudioc |
1.17 |
signal region (region $D$). For more information about
|
| 17 |
|
|
this one candidate events, see Appendix~\ref{sec:cand}.
|
| 18 |
benhoob |
1.29 |
The Standard Model MC expectation is 1.3 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 |
1.32 |
The prediction of the ABCD method is is given by $A \times C / B = 1.3 \pm 0.8({\rm stat}) \pm 0.3({\rm syst})$.
|
| 26 |
claudioc |
1.2 |
|
| 27 |
claudioc |
1.1 |
\begin{table}[hbt]
|
| 28 |
|
|
\begin{center}
|
| 29 |
|
|
\caption{\label{tab:datayield} Data yields in the four
|
| 30 |
benhoob |
1.7 |
regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given
|
| 31 |
benhoob |
1.29 |
by A $\times$ C / B. The quoted uncertainty
|
| 32 |
benhoob |
1.4 |
on the prediction in data is statistical only, assuming Gaussian errors.
|
| 33 |
benhoob |
1.32 |
We also show the SM Monte Carlo expectations, scaled to 34.0~pb$^{-1}$.}
|
| 34 |
benhoob |
1.7 |
\begin{tabular}{l||c|c|c|c||c}
|
| 35 |
benhoob |
1.32 |
%%%official json v3 33.96/pb, 38X MC (D6T for ttbar and DY)
|
| 36 |
benhoob |
1.7 |
\hline
|
| 37 |
benhoob |
1.32 |
sample & A & B & C & D & PRED \\
|
| 38 |
benhoob |
1.24 |
\hline
|
| 39 |
benhoob |
1.32 |
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 8.44 $\pm$ 0.18 & 32.83 $\pm$ 0.35 & 4.78 $\pm$ 0.14 & 1.07 $\pm$ 0.06 & 1.23 $\pm$ 0.05 \\
|
| 40 |
|
|
$t\bar{t}\rightarrow \mathrm{other}$ & 0.12 $\pm$ 0.02 & 0.78 $\pm$ 0.05 & 0.16 $\pm$ 0.02 & 0.02 $\pm$ 0.01 & 0.02 $\pm$ 0.01 \\
|
| 41 |
|
|
$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.17 $\pm$ 0.08 & 1.18 $\pm$ 0.22 & 0.04 $\pm$ 0.04 & 0.12 $\pm$ 0.07 & 0.01 $\pm$ 0.01 \\
|
| 42 |
|
|
$W^{\pm}$ + jets & 0.00 $\pm$ 0.00 & 0.09 $\pm$ 0.09 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
|
| 43 |
|
|
$W^+W^-$ & 0.11 $\pm$ 0.01 & 0.29 $\pm$ 0.02 & 0.02 $\pm$ 0.01 & 0.03 $\pm$ 0.01 & 0.01 $\pm$ 0.00 \\
|
| 44 |
benhoob |
1.26 |
$W^{\pm}Z^0$ & 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 \\
|
| 45 |
|
|
$Z^0Z^0$ & 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 \\
|
| 46 |
benhoob |
1.32 |
single top & 0.29 $\pm$ 0.01 & 1.04 $\pm$ 0.03 & 0.04 $\pm$ 0.01 & 0.01 $\pm$ 0.00 & 0.01 $\pm$ 0.00 \\
|
| 47 |
benhoob |
1.7 |
\hline
|
| 48 |
benhoob |
1.32 |
total SM MC & 9.14 $\pm$ 0.20 & 36.26 $\pm$ 0.43 & 5.05 $\pm$ 0.14 & 1.27 $\pm$ 0.10 & 1.27 $\pm$ 0.05 \\
|
| 49 |
claudioc |
1.1 |
\hline
|
| 50 |
benhoob |
1.32 |
data & 12 & 37 & 4 & 1 & 1.30 $\pm$ 0.78 \\
|
| 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 |
claudioc |
1.22 |
%\clearpage
|
| 63 |
benhoob |
1.10 |
|
| 64 |
claudioc |
1.2 |
\subsection{Background estimate from the $P_T(\ell\ell)$ method}
|
| 65 |
|
|
\label{sec:victoryres}
|
| 66 |
|
|
|
| 67 |
benhoob |
1.11 |
We first use the $P_T(\ell \ell)$ method to predict the number of events
|
| 68 |
benhoob |
1.12 |
in control region A, defined in Sec.~\ref{sec:abcd} as
|
| 69 |
benhoob |
1.19 |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$.
|
| 70 |
benhoob |
1.12 |
We count the number of events in region
|
| 71 |
|
|
$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$
|
| 72 |
|
|
cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$,
|
| 73 |
benhoob |
1.32 |
and find $N_{A'}=5$. We subtract off the expected DY contribution in this region
|
| 74 |
|
|
$N_{DY} = 1.3 \pm 0.9$, derived in Sec.~\ref{sec:othBG}.
|
| 75 |
benhoob |
1.15 |
To predict the yield in region A we take
|
| 76 |
benhoob |
1.32 |
$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 9.0 \pm 6.0$
|
| 77 |
|
|
where we have taken $K = 1.7$ and $K_C = 1.4$.
|
| 78 |
benhoob |
1.27 |
This uncertainty takes into account the statistical uncertainties in $N_{A'}$ and $N_{DY}$,
|
| 79 |
benhoob |
1.32 |
assuming Gaussian errors. This yield is consistent
|
| 80 |
|
|
with the observed yield of 12 events, as shown in
|
| 81 |
benhoob |
1.29 |
Table~\ref{tab:victory} and displayed in Fig.~\ref{fig:victory} (left).
|
| 82 |
benhoob |
1.11 |
|
| 83 |
|
|
Encouraged by the good agreement between predicted and observed yields
|
| 84 |
|
|
in the control region, we proceed to perform the $P_T(\ell \ell)$ method
|
| 85 |
|
|
in the signal region ${\rm SumJetPt}>300$~GeV.
|
| 86 |
claudioc |
1.2 |
The number of data events in region $D'$, which is defined in
|
| 87 |
|
|
Section~\ref{sec:othBG} to be the same as region $D$ but with the
|
| 88 |
|
|
$\met/\sqrt{\rm SumJetPt}$ requirement
|
| 89 |
benhoob |
1.11 |
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement,
|
| 90 |
benhoob |
1.32 |
is $N_{D'}=1$.
|
| 91 |
benhoob |
1.27 |
%We next subtract off the expected DY contribution of
|
| 92 |
|
|
%$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated
|
| 93 |
|
|
%in Sec.~\ref{sec:othBG}.
|
| 94 |
|
|
The BG prediction is
|
| 95 |
benhoob |
1.32 |
$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.1 \pm 2.1({\rm stat}) \pm 0.6({\rm syst})$
|
| 96 |
|
|
where $K=1.5$ as derived in Sec.~\ref{sec:victory} and $K_C = 1.4 \pm 0.4$.
|
| 97 |
|
|
This prediction is consistent with the observed yield of 1 event, as summarized
|
| 98 |
|
|
in Table~\ref{tab:victory} and Fig.~\ref{fig:victory} (right).
|
| 99 |
benhoob |
1.11 |
|
| 100 |
claudioc |
1.1 |
|
| 101 |
claudioc |
1.3 |
\begin{figure}[hbt]
|
| 102 |
|
|
\begin{center}
|
| 103 |
benhoob |
1.32 |
\includegraphics[width=0.48\linewidth]{victory_control_v3.png}
|
| 104 |
|
|
\includegraphics[width=0.48\linewidth]{victory_signal_v3.png}
|
| 105 |
claudioc |
1.3 |
\caption{\label{fig:victory}\protect Distributions of
|
| 106 |
|
|
tcMet/$\sqrt{\rm SumJetPt}$ for the control and signal region.
|
| 107 |
|
|
We show the oberved distributions in both Monte Carlo and data.
|
| 108 |
|
|
We also show the distributions predicted from
|
| 109 |
benhoob |
1.4 |
${P_T(\ell\ell)}/\sqrt{\rm SumJetPt}$ in both MC and data.}
|
| 110 |
claudioc |
1.3 |
\end{center}
|
| 111 |
|
|
\end{figure}
|
| 112 |
|
|
|
| 113 |
|
|
|
| 114 |
benhoob |
1.9 |
\begin{table}[hbt]
|
| 115 |
|
|
\begin{center}
|
| 116 |
benhoob |
1.27 |
\caption{\label{tab:victory}Results of the dilepton $p_{T}$ template method in the control region
|
| 117 |
|
|
($125 < \mathrm{SumJetPt} < 300$~GeV) and signal region ($\mathrm{SumJetPt} > 300$~GeV). The predicted and
|
| 118 |
|
|
observed yields for the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}} > 8.5$~GeV$^{1/2}$. The errors are
|
| 119 |
benhoob |
1.32 |
statistical only, assuming Gaussian errors. Note that the correction factor $K_C$ has been applied to
|
| 120 |
benhoob |
1.33 |
the data but not to the MC. }
|
| 121 |
benhoob |
1.29 |
\begin{tabular}{l|cc|cc}
|
| 122 |
benhoob |
1.27 |
\hline
|
| 123 |
|
|
& Control Region & & Signal Region & \\
|
| 124 |
benhoob |
1.9 |
\hline
|
| 125 |
benhoob |
1.27 |
& Predicted & Observed & Predicted & Observed \\
|
| 126 |
benhoob |
1.9 |
\hline
|
| 127 |
benhoob |
1.32 |
total SM MC & 6.45 & 9.14 & 0.92 & 1.27 \\
|
| 128 |
|
|
data & $9.0 \pm 6.0$ & 12 & $2.1 \pm 2.1$ & 1 \\
|
| 129 |
benhoob |
1.9 |
\hline
|
| 130 |
|
|
\end{tabular}
|
| 131 |
|
|
\end{center}
|
| 132 |
|
|
\end{table}
|
| 133 |
|
|
|
| 134 |
|
|
|
| 135 |
|
|
|
| 136 |
claudioc |
1.22 |
% \clearpage
|
| 137 |
claudioc |
1.3 |
\subsection{Summary of results}
|
| 138 |
benhoob |
1.21 |
|
| 139 |
benhoob |
1.28 |
In summary, in the signal region defined as $\mathrm{SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt} > 8.5$~GeV$^{1/2}$:\\
|
| 140 |
benhoob |
1.21 |
We observe 1 event. \\
|
| 141 |
benhoob |
1.28 |
We expect 1.3 events from Standard Model MC prediction. \\
|
| 142 |
benhoob |
1.32 |
The ABCD data driven method predicts $1.3 \pm 0.8({\rm stat}) \pm 0.3({\rm syst})$ events. \\
|
| 143 |
benhoob |
1.33 |
The $P_T(\ell\ell)$ method predicts $2.1 \pm 2.1({\rm stat}) \pm 0.6({\rm syst})$ events. \\
|
| 144 |
benhoob |
1.21 |
|
| 145 |
|
|
All three background estimates are consistent within their uncertainties.
|
| 146 |
|
|
We thus take as our best estimate of the Standard Model yield in
|
| 147 |
benhoob |
1.32 |
the signal region the average of the predicted yields from the 2 data-driven methods,
|
| 148 |
|
|
weighted by their uncertainties.
|
| 149 |
|
|
This procedure gives an expected background yield $N_{BG}=1.4 \pm 0.8$.
|
| 150 |
benhoob |
1.21 |
|
| 151 |
|
|
We conclude that we see no evidence for an anomalous
|
| 152 |
claudioc |
1.1 |
rate of opposite sign isolated dilepton events
|
| 153 |
|
|
at high \met and high SumJetPt. The extraction of
|
| 154 |
|
|
quantitative limits on new physics models is discussed
|
| 155 |
benhoob |
1.5 |
in Section~\ref{sec:limit}. |
