| 27 |
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Thus, we can use an ABCD method in the \met$/\sqrt{\rm SumJetPt}$ vs |
| 28 |
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sumJetPt plane to estimate the background in a data driven way. |
| 29 |
|
|
| 30 |
< |
\begin{figure}[tb] |
| 30 |
> |
\begin{figure}[bht] |
| 31 |
|
\begin{center} |
| 32 |
|
\includegraphics[width=0.75\linewidth]{uncorrelated.pdf} |
| 33 |
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\caption{\label{fig:uncor}\protect Distributions of SumJetPt |
| 36 |
|
\end{center} |
| 37 |
|
\end{figure} |
| 38 |
|
|
| 39 |
< |
\begin{figure}[bt] |
| 39 |
> |
\begin{figure}[tb] |
| 40 |
|
\begin{center} |
| 41 |
|
\includegraphics[width=0.5\linewidth, angle=90]{abcdMC.pdf} |
| 42 |
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\caption{\label{fig:abcdMC}\protect Distributions of SumJetPt |
| 56 |
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%wrt changes in regions. I am not sure that we have done it and |
| 57 |
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%I am not sure it is necessary (Claudio).} |
| 58 |
|
|
| 59 |
< |
\begin{table}[htb] |
| 59 |
> |
\begin{table}[ht] |
| 60 |
|
\begin{center} |
| 61 |
|
\caption{\label{tab:abcdMC} Expected SM Monte Carlo yields for |
| 62 |
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35 pb$^{-1}$ in the ABCD regions, as well as the predicted yield in |
| 251 |
|
\caption{\label{tab:sigcont} Effects of signal contamination |
| 252 |
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for the two data-driven background estimates. The three columns give |
| 253 |
|
the expected yield in the signal region and the background estimates |
| 254 |
< |
using the ABCD and $P_T(\ell \ell)$ methods. Results are normalized to 35~pb$^{-1}$. |
| 255 |
< |
{\color{red} \bf UPDATE RESULTS WITH DY SAMPLES.} |
| 254 |
> |
using the ABCD and $P_T(\ell \ell)$ methods. Results are normalized to 35~pb$^{-1}$.} |
| 255 |
|
\begin{tabular}{lccc} |
| 256 |
|
\hline |
| 257 |
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& Yield & ABCD & $P_T(\ell \ell)$ \\ |
| 258 |
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\hline |
| 259 |
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SM only & 1.43 & 1.19 & 1.03 \\ |
| 260 |
< |
SM + LM0 & 7.88X & 4.24X & 2.28X \\ |
| 261 |
< |
SM + LM1 & 3.98X & 1.53X & 1.44X \\ |
| 260 |
> |
SM + LM0 & 7.90 & 4.23 & 2.35 \\ |
| 261 |
> |
SM + LM1 & 4.00 & 1.53 & 1.51 \\ |
| 262 |
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\hline |
| 263 |
|
\end{tabular} |
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|
\end{center} |
