| 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} |
| 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 |
| 27 |
< |
Gaussian errors), as shown in Table~\ref{tab:datayield}. |
| 27 |
> |
is $1.5 \pm 0.9(stat) \pm 0.2(syst)$ events, as shown in Table~\ref{tab:datayield}. |
| 28 |
|
|
| 29 |
|
\begin{table}[hbt] |
| 30 |
|
\begin{center} |
| 31 |
|
\caption{\label{tab:datayield} Data yields in the four |
| 32 |
|
regions of Figure~\ref{fig:abcdData}, as well as the predicted yield in region D given |
| 33 |
< |
by A$\times$C / B. The quoted uncertainty |
| 33 |
> |
by A $\times$C / B. The quoted uncertainty |
| 34 |
|
on the prediction in data is statistical only, assuming Gaussian errors. |
| 35 |
|
We also show the SM Monte Carlo expectations, scaled to 34.85~pb$^{-1}$.} |
| 36 |
|
\begin{tabular}{l||c|c|c|c||c} |
| 37 |
|
\hline |
| 38 |
< |
sample & A & B & C & D & A$\times$C / B \\ |
| 38 |
> |
sample & A & B & C & D & A $\times$ C / B \\ |
| 39 |
|
\hline |
| 40 |
+ |
|
| 41 |
|
$t\bar{t}\rightarrow \ell^{+}\ell^{-}$ & 7.96 & 33.07 & 4.81 & 1.20 & 1.16 \\ |
| 42 |
< |
$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\ |
| 43 |
< |
$Z^0$ + jets & 0.00 & 1.16 & 0.08 & 0.08 & 0.00 \\ |
| 44 |
< |
$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\ |
| 45 |
< |
$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\ |
| 46 |
< |
$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\ |
| 47 |
< |
$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\ |
| 48 |
< |
single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\ |
| 42 |
> |
$t\bar{t}\rightarrow \mathrm{other}$ & 0.15 & 0.85 & 0.09 & 0.04 & 0.02 \\ |
| 43 |
> |
$Z^0 \rightarrow \ell^{+}\ell^{-}$ & 0.03 & 1.47 & 0.10 & 0.10 & 0.00 \\ |
| 44 |
> |
$W^{\pm}$ + jets & 0.00 & 0.10 & 0.00 & 0.00 & 0.00 \\ |
| 45 |
> |
$W^+W^-$ & 0.19 & 0.29 & 0.02 & 0.07 & 0.02 \\ |
| 46 |
> |
$W^{\pm}Z^0$ & 0.03 & 0.04 & 0.01 & 0.01 & 0.00 \\ |
| 47 |
> |
$Z^0Z^0$ & 0.00 & 0.03 & 0.00 & 0.00 & 0.00 \\ |
| 48 |
> |
single top & 0.28 & 1.00 & 0.04 & 0.01 & 0.01 \\ |
| 49 |
|
\hline |
| 50 |
< |
total SM MC & 8.61 & 36.54 & 5.05 & 1.41 & 1.19 \\ |
| 50 |
> |
total SM MC & 8.63 & 36.85 & 5.07 & 1.43 & 1.19 \\ |
| 51 |
|
\hline |
| 52 |
< |
data & 11 & 36 & 5 & 1 &1.53 $\pm$ 0.86 \\ |
| 52 |
> |
data & 11 & 36 & 5 & 1 & $1.53 \pm 0.86$ \\ |
| 53 |
|
\hline |
| 54 |
|
\end{tabular} |
| 55 |
|
\end{center} |
| 68 |
|
|
| 69 |
|
We first use the $P_T(\ell \ell)$ method to predict the number of events |
| 70 |
|
in control region A, defined in Sec.~\ref{sec:abcd} as |
| 71 |
< |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5. |
| 71 |
> |
$125<{\rm SumJetPt}>300$~GeV and $\met/\sqrt{\rm SumJetPt}>$8.5~GeV$^{1/2}$. |
| 72 |
|
We count the number of events in region |
| 73 |
|
$A'$, defined in Sec.~\ref{sec:othBG} by replacing the above $\met/\sqrt{\rm SumJetPt}$ |
| 74 |
|
cut with the same cut on the quantity $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$, |
| 75 |
< |
and find $N_{A'}=6$. To predict the yield in region A we take |
| 76 |
< |
$N_A = K \cdot K_C \cdot N_{A'} = 10.4 \pm 4.2$ |
| 75 |
> |
and find $N_{A'}=6$. We subtract off the expected DY contribution in this region |
| 76 |
> |
$N_{DY} = 2.5 \pm 2.4$, derived in Sec.~\ref{sec:othBG}. |
| 77 |
> |
To predict the yield in region A we take |
| 78 |
> |
$N_A = K \cdot K_C \cdot ( N_{A'} - N_{DY} ) = 6.1 \pm 6.0$ |
| 79 |
|
(statistical uncertainty only, assuming Gaussian errors), |
| 80 |
< |
where we have taken $K = 1.73$ and $K_C = 1$. This yield is in good |
| 81 |
< |
agreement with the observed yield of 11 events, as shown in |
| 80 |
> |
where we have taken $K = 1.73$ and $K_C = 1$. This yield is consistent |
| 81 |
> |
with the observed yield of 11 events, as shown in |
| 82 |
|
Table~\ref{tab:victory_control} and displayed in Fig.~\ref{fig:victory} (left). |
| 80 |
– |
{\color{red} \bf Perform DY estimate for this control region}. |
| 83 |
|
|
| 84 |
|
Encouraged by the good agreement between predicted and observed yields |
| 85 |
|
in the control region, we proceed to perform the $P_T(\ell \ell)$ method |
| 90 |
|
replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement, |
| 91 |
|
is $N_{D'}=2$. |
| 92 |
|
We next subtract off the expected DY contribution of |
| 93 |
< |
{\color{red} \bf $N_{DY}$ = 0.8 $\pm$ 0.8 (update DY estimate)} events, as calculated |
| 93 |
> |
$N_{DY}$ = $0.4 \pm 0.4$ events, as calculated |
| 94 |
|
in Sec.~\ref{sec:othBG}. The BG prediction is |
| 95 |
< |
$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 1.8^{+2.5}_{-1.8}$ (statistical |
| 95 |
> |
$N_D = K \cdot K_C \cdot (N_{D'}-N_{DY}) = 2.5 \pm 2.2$ (statistical |
| 96 |
|
uncertainty only, assuming Gaussian errors), where $K=1.54 \pm xx$ |
| 97 |
|
as derived in Sec.~\ref{sec:victory} and $K_C = 1$. |
| 98 |
|
This prediction is consistent with the observed yield of |
| 113 |
|
\end{figure} |
| 114 |
|
|
| 115 |
|
|
| 116 |
+ |
|
| 117 |
|
\begin{table}[hbt] |
| 118 |
|
\begin{center} |
| 119 |
|
\caption{\label{tab:victory_control}Results of the dilepton $p_{T}$ template method in the control region |
| 120 |
< |
$125 < \mathrm{sumJetPt} < 300$~GeV. The predicted and observed yields for |
| 120 |
> |
$125 < \mathrm{sumJetPt} < 300$~GeV$^{1/2}$. The predicted and observed yields for |
| 121 |
|
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
| 122 |
|
and MC. The error on the prediction for data is statistical only, assuming |
| 123 |
|
Gaussian errors.} |
| 125 |
|
\hline |
| 126 |
|
& Predicted & Observed & Obs/Pred \\ |
| 127 |
|
\hline |
| 128 |
< |
total SM MC & 7.10 & 8.61 & 1.21 \\ |
| 129 |
< |
data & 10.38 $\pm$ 4.24 & 11 & 1.06 \\ |
| 128 |
> |
total SM MC & 7.18 & 8.63 & 1.20 \\ |
| 129 |
> |
data & $6.06 \pm 5.95$ & 11 & 1.82 \\ |
| 130 |
|
\hline |
| 131 |
|
\end{tabular} |
| 132 |
|
\end{center} |
| 135 |
|
\begin{table}[hbt] |
| 136 |
|
\begin{center} |
| 137 |
|
\caption{\label{tab:victory_signal}Results of the dilepton $p_{T}$ template method in the signal region |
| 138 |
< |
$\mathrm{sumJetPt} > 300$~GeV. The predicted and observed yields for |
| 138 |
> |
$\mathrm{sumJetPt} > 300$~GeV$^{1/2}$. The predicted and observed yields for |
| 139 |
|
the region $\mathrm{tcmet}/\sqrt{\mathrm{sumJetPt}}>$~8.5 are shown for data |
| 140 |
|
and MC. The error on the prediction for data is statistical only, assuming |
| 141 |
|
Gaussian errors.} |
| 143 |
|
\hline |
| 144 |
|
& Predicted & Observed & Obs/Pred \\ |
| 145 |
|
\hline |
| 146 |
< |
total SM MC & 0.96 & 1.41 & 1.46 \\ |
| 147 |
< |
data & $1.8^{+2.5}_{-1.8}$ & 1 & 0.56 \\ |
| 146 |
> |
total SM MC & 1.03 & 1.43 & 1.38 \\ |
| 147 |
> |
data & $2.53 \pm 2.25$ & 1 & 0.40 \\ |
| 148 |
|
\hline |
| 149 |
|
\end{tabular} |
| 150 |
|
\end{center} |
