| 27 |
|
Thus, we can use an ABCD method in the \met$/\sqrt{\rm SumJetPt}$ vs |
| 28 |
|
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 |
|
\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 |
|
\caption{\label{fig:abcdMC}\protect Distributions of SumJetPt |
| 56 |
|
%wrt changes in regions. I am not sure that we have done it and |
| 57 |
|
%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 |
|
35 pb$^{-1}$ in the ABCD regions, as well as the predicted yield in |
