| 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}[bht] |
| 31 |
+ |
%\begin{center} |
| 32 |
+ |
%\includegraphics[width=0.75\linewidth]{uncorrelated.pdf} |
| 33 |
+ |
%\caption{\label{fig:uncor}\protect Distributions of SumJetPt |
| 34 |
+ |
%in MC $t\bar{t}$ events for different intervals of |
| 35 |
+ |
%MET$/\sqrt{\rm SumJetPt}$.} |
| 36 |
+ |
%\end{center} |
| 37 |
+ |
%\end{figure} |
| 38 |
+ |
|
| 39 |
|
\begin{figure}[bht] |
| 40 |
|
\begin{center} |
| 41 |
< |
\includegraphics[width=0.75\linewidth]{uncorrelated.pdf} |
| 41 |
> |
\includegraphics[width=0.75\linewidth]{uncor.png} |
| 42 |
|
\caption{\label{fig:uncor}\protect Distributions of SumJetPt |
| 43 |
|
in MC $t\bar{t}$ events for different intervals of |
| 44 |
< |
MET$/\sqrt{\rm SumJetPt}$.} |
| 44 |
> |
MET$/\sqrt{\rm SumJetPt}$. h1, h2, and h3 refer to the MET$/\sqrt{\rm SumJetPt}$ |
| 45 |
> |
intervals 4.5-6.5, 6.5-8.5 and $>$8.5, respectively.} |
| 46 |
|
\end{center} |
| 47 |
|
\end{figure} |
| 48 |
|
|
| 59 |
|
The signal region is region D. The expected number of events |
| 60 |
|
in the four regions for the SM Monte Carlo, as well as the BG |
| 61 |
|
prediction AC/B are given in Table~\ref{tab:abcdMC} for an integrated |
| 62 |
< |
luminosity of 35 pb$^{-1}$. The ABCD method is accurate |
| 63 |
< |
to about 20\%, and we assess a corresponding systematic uncertainty on |
| 64 |
< |
the background prediction. |
| 62 |
> |
luminosity of 35 pb$^{-1}$. The ABCD method with chosen boundaries is accurate |
| 63 |
> |
to about 20\%. As shown in Table~\ref{tab:abcdsyst}, we assess systematic uncertainties |
| 64 |
> |
by varying the boundaries by an amount consistent with the hadronic energy scale uncertainty, |
| 65 |
> |
which we take as $\pm$5\% for SumJetPt and $\pm$2.5\% for MET/$\sqrt{\rm SumJetPt}$, since the |
| 66 |
> |
uncertainty on this quantity partially cancels due to the fact that it is a ratio of correlated |
| 67 |
> |
quantities. Based on these studies we assess a correction factor $k_{ABCD} = 1.2 \pm 0.2$ to the |
| 68 |
> |
predicted yield using the ABCD method. |
| 69 |
> |
|
| 70 |
> |
|
| 71 |
|
%{\color{red} Avi wants some statement about stability |
| 72 |
|
%wrt changes in regions. I am not sure that we have done it and |
| 73 |
|
%I am not sure it is necessary (Claudio).} |
| 94 |
|
\hline |
| 95 |
|
\end{tabular} |
| 96 |
|
\end{center} |
| 97 |
+ |
\end{table} |
| 98 |
+ |
|
| 99 |
+ |
|
| 100 |
+ |
|
| 101 |
+ |
\begin{table}[ht] |
| 102 |
+ |
\begin{center} |
| 103 |
+ |
\caption{\label{tab:abcdsyst} Results of the systematic study of the ABCD method by varying the boundaries |
| 104 |
+ |
between the ABCD regions shown in Fig.~\ref{fig:abcdMC}. Here $x_1$ is the lower SumJetPt boundary and |
| 105 |
+ |
$x_2$ is the boundary separating regions A and B from C and D, their nominal values are 125 and 300~GeV, |
| 106 |
+ |
respectively. $y_1$ is the lower MET/$\sqrt{\rm SumJetPt}$ boundary and |
| 107 |
+ |
$y_2$ is the boundary separating regions B and C from A and D, their nominal values are 4.5 and 8.5~GeV$^{1/2}$, |
| 108 |
+ |
respectively.} |
| 109 |
+ |
\begin{tabular}{cccc|c} |
| 110 |
+ |
\hline |
| 111 |
+ |
$x_1$ & $x_2$ & $y_1$ & $y_2$ & Observed/Predicted \\ |
| 112 |
+ |
\hline |
| 113 |
+ |
nominal & nominal & nominal & nominal & 1.20 \\ |
| 114 |
+ |
+5\% & +5\% & +2.5\% & +2.5\% & 1.38 \\ |
| 115 |
+ |
+5\% & +5\% & nominal & nominal & 1.31 \\ |
| 116 |
+ |
nominal & nominal & +2.5\% & +2.5\% & 1.25 \\ |
| 117 |
+ |
nominal & +5\% & nominal & +2.5\% & 1.32 \\ |
| 118 |
+ |
nominal & -5\% & nominal & -2.5\% & 1.16 \\ |
| 119 |
+ |
-5\% & -5\% & +2.5\% & +2.5\% & 1.21 \\ |
| 120 |
+ |
+5\% & +5\% & -2.5\% & -2.5\% & 1.26 \\ |
| 121 |
+ |
\hline |
| 122 |
+ |
\end{tabular} |
| 123 |
+ |
\end{center} |
| 124 |
|
\end{table} |
| 125 |
|
|
| 126 |
|
\subsection{Dilepton $P_T$ method} |
