| 3 |
|
We have developed two data-driven methods to |
| 4 |
|
estimate the background in the signal region. |
| 5 |
|
The first one exploits the fact that |
| 6 |
< |
\met and \met$/\sqrt{\rm SumJetPt}$ are nearly |
| 6 |
> |
SumJetPt and \met$/\sqrt{\rm SumJetPt}$ are nearly |
| 7 |
|
uncorrelated for the $t\bar{t}$ background |
| 8 |
|
(Section~\ref{sec:abcd}); the second one |
| 9 |
|
is based on the fact that in $t\bar{t}$ the |
| 21 |
|
\subsection{ABCD method} |
| 22 |
|
\label{sec:abcd} |
| 23 |
|
|
| 24 |
< |
We find that in $t\bar{t}$ events \met and |
| 24 |
> |
We find that in $t\bar{t}$ events SumJetPt and |
| 25 |
|
\met$/\sqrt{\rm SumJetPt}$ are nearly uncorrelated, |
| 26 |
|
as demonstrated in Figure~\ref{fig:uncor}. |
| 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 |
| 43 |
< |
vs. MET$/\sqrt{\rm SumJetPt}$ for SM Monte Carlo. Here we also |
| 44 |
< |
show our choice of ABCD regions.} |
| 42 |
> |
\caption{\label{fig:abcdMC}\protect Distributions of MET$/\sqrt{\rm SumJetPt}$ vs. |
| 43 |
> |
SumJetPt for SM Monte Carlo. Here we also show our choice of ABCD regions.} |
| 44 |
|
\end{center} |
| 45 |
|
\end{figure} |
| 46 |
|
|
| 55 |
|
%wrt changes in regions. I am not sure that we have done it and |
| 56 |
|
%I am not sure it is necessary (Claudio).} |
| 57 |
|
|
| 58 |
< |
\begin{table}[htb] |
| 58 |
> |
\begin{table}[ht] |
| 59 |
|
\begin{center} |
| 60 |
|
\caption{\label{tab:abcdMC} Expected SM Monte Carlo yields for |
| 61 |
|
35 pb$^{-1}$ in the ABCD regions, as well as the predicted yield in |
| 120 |
|
$P_T(\ell\ell)$: |
| 121 |
|
\begin{itemize} |
| 122 |
|
\item $Ws$ in top events are polarized. Neutrinos are emitted preferentially |
| 123 |
< |
forward in the $W$ rest frame, thus the $P_T(\nu\nu)$ distribution is harder |
| 123 |
> |
parallel to the $W$ velocity while charged leptons are emitted prefertially |
| 124 |
> |
anti-parallel. Thus the $P_T(\nu\nu)$ distribution is harder |
| 125 |
|
than the $P_T(\ell\ell)$ distribution for top dilepton events. |
| 126 |
|
\item The lepton selections results in $P_T$ and $\eta$ cuts on the individual |
| 127 |
|
leptons that have no simple correspondance to the neutrino requirements. |
| 251 |
|
\caption{\label{tab:sigcont} Effects of signal contamination |
| 252 |
|
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 |
|
& Yield & ABCD & $P_T(\ell \ell)$ \\ |
