| 9 |
|
|
| 10 |
|
Dilepton \ttbar\ events have 2 jets from the top decays, so additional |
| 11 |
|
jets from radiation or higher order contributions are required to |
| 12 |
< |
enter the signal sample. The modeling of addtional jets in \ttbar\ |
| 12 |
> |
enter the signal sample. The modeling of additional jets in \ttbar\ |
| 13 |
|
events is checked in a \ttll\ control sample, |
| 14 |
|
selected by requiring |
| 15 |
|
\begin{itemize} |
| 16 |
|
\item exactly 2 selected electrons or muons with \pt $>$ 20 GeV |
| 17 |
< |
\item \met\ $>$ 100 GeV |
| 17 |
> |
\item \met\ $>$ 50 GeV |
| 18 |
|
\item $\geq1$ b-tagged jet |
| 19 |
|
\item Z-veto ($|m_{\ell\ell} - 91| > 15$ GeV) |
| 20 |
|
\end{itemize} |
| 35 |
|
|
| 36 |
|
\begin{figure}[hbt] |
| 37 |
|
\begin{center} |
| 38 |
< |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met100_mueg.pdf} |
| 39 |
< |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met100_diel.pdf}% |
| 40 |
< |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met100_dimu.pdf} |
| 38 |
> |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met50_mueg.pdf} |
| 39 |
> |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met50_diel.pdf}% |
| 40 |
> |
\includegraphics[width=0.5\linewidth]{plots/njets_all_met50_dimu.pdf} |
| 41 |
|
\caption{ |
| 42 |
|
\label{fig:dileptonnjets}%\protect |
| 43 |
|
Comparison of the jet multiplicity distribution in data and MC for dilepton events in the \E-\M\ |
| 131 |
|
\noindent This insures that $K_3 M_3/(M_2 + K_3 M_3 + K_4 M_4) = N_3 / |
| 132 |
|
(N_2+N_3+N_4)$ and similarly for the $\geq 4$ jet bin. |
| 133 |
|
|
| 134 |
+ |
Table~\ref{tab:njetskfactors} also shows the values of $K_3$ and $K_4$ when the \met\ cut in the control sample definition is changed from 50 GeV to 100 GeV and 150 GeV. |
| 135 |
+ |
These values of $K_3$ and $K_4$ are not used in the analysis, but demonstrate that there is no statistically significant dependence of $K_3$ and $K_4$ on the \met\ cut. |
| 136 |
|
|
| 137 |
< |
The factors $K_3$ and $K_4$ are applied to the \ttll\ MC throughout the |
| 137 |
> |
|
| 138 |
> |
The factors $K_3$ and $K_4$ (derived with the 50 GeV \met\ cut) are applied to the \ttll\ MC throughout the |
| 139 |
|
entire analysis, i.e. |
| 140 |
|
whenever \ttll\ MC is used to estimate or subtract |
| 141 |
|
a yield or distribution. |
| 150 |
|
|
| 151 |
|
\begin{table}[!ht] |
| 152 |
|
\begin{center} |
| 153 |
< |
\begin{tabular}{l|c} |
| 153 |
> |
\begin{tabular}{l|c|c|c} |
| 154 |
> |
\cline{2-4} |
| 155 |
> |
& \multicolumn{3}{c}{ \met\ cut for data/MC scale factors} \\ |
| 156 |
|
\hline |
| 157 |
< |
Jet Multiplicity Sample |
| 153 |
< |
& Data/MC Scale Factor \\ |
| 157 |
> |
Jet Multiplicity Sample & 50 GeV & 100 GeV & 150 GeV \\ |
| 158 |
|
\hline |
| 159 |
|
\hline |
| 160 |
< |
N jets $= 3$ (sensitive to $\ttbar+1$ extra jet from radiation) & |
| 161 |
< |
$K_3 = 1.01 \pm 0.03$\\ |
| 160 |
> |
N jets $= 3$ (sensitive to $\ttbar+1$ extra jet from radiation) |
| 161 |
> |
& $K_3 = 0.98 \pm 0.02$ & $K_3 = 1.01 \pm 0.03$ & $K_3 = 1.00 \pm 0.08$ \\ |
| 162 |
|
N jets $\ge4$ (sensitive to $\ttbar+\ge2$ extra jets from radiation) |
| 163 |
< |
& $K_4 = 0.93 \pm 0.04$\\ |
| 163 |
> |
& $K_4 = 0.94 \pm 0.02$ & $K_4 = 0.93 \pm 0.04$ & $K_4 = 1.00 \pm 0.08$ \\ |
| 164 |
|
\hline |
| 165 |
|
\end{tabular} |
| 166 |
|
\caption{Data/MC scale factors used to account for differences in the |
| 167 |
|
fraction of events with additional hard jets from radiation in |
| 168 |
< |
\ttll\ events. \label{tab:njetskfactors}} |
| 168 |
> |
\ttll\ events. The values derived with the 50 GeV \met\ cut are applied |
| 169 |
> |
to the \ttll\ MC throughout the analysis. \label{tab:njetskfactors}} |
| 170 |
|
\end{center} |
| 171 |
|
\end{table} |
| 172 |
|
|
| 232 |
|
CR4D & CR4E & CR4F\\ |
| 233 |
|
\hline |
| 234 |
|
\hline |
| 235 |
< |
$\mu$ MC & $256 \pm 5$ & $152 \pm 4$ & $91 \pm 3$ & $26 \pm 2$ & $6 \pm 1$ & $4 \pm 1$ & $2 \pm 1$ \\ |
| 235 |
> |
$\mu$ MC & $256 \pm 14$ & $152 \pm 11$ & $91 \pm 9$ & $26 \pm 5$ & $6 \pm 2$ & $4 \pm 2$ & $2 \pm 1$ \\ |
| 236 |
|
$\mu$ Data & $251$ & $156$ & $98$ & $27$ & $8$ & $6$ & $4$ \\ |
| 237 |
|
\hline |
| 238 |
< |
$\mu$ Data/MC SF & $0.98 \pm 0.07$ & $1.02 \pm 0.09$ & $1.08 \pm 0.12$ & $1.04 \pm 0.21$ & $1.29 \pm 0.48$ & $1.35 \pm 0.59$ & $2.10 \pm 1.28$ \\ |
| 238 |
> |
$\mu$ Data/MC SF & $0.98 \pm 0.08$ & $1.02 \pm 0.11$ & $1.08 \pm 0.16$ & $1.04 \pm 0.28$ & $1.29 \pm 0.65$ & $1.35 \pm 0.80$ & $2.10 \pm 1.72$ \\ |
| 239 |
|
\hline |
| 240 |
|
\hline |
| 241 |
< |
e MC & $227 \pm 5$ & $139 \pm 4$ & $73 \pm 3$ & $21 \pm 1$ & $5 \pm 1$ & $2 \pm 0$ & $1 \pm 0$ \\ |
| 241 |
> |
e MC & $227 \pm 13$ & $139 \pm 11$ & $73 \pm 8$ & $21 \pm 4$ & $5 \pm 2$ & $2 \pm 1$ & $1 \pm 1$ \\ |
| 242 |
|
e Data & $219$ & $136$ & $72$ & $19$ & $2$ & $1$ & $1$ \\ |
| 243 |
|
\hline |
| 244 |
< |
e Data/MC SF & $0.96 \pm 0.07$ & $0.98 \pm 0.09$ & $0.99 \pm 0.12$ & $0.92 \pm 0.22$ & $0.41 \pm 0.29$ & $0.53 \pm 0.54$ & $0.76 \pm 0.78$ \\ |
| 244 |
> |
e Data/MC SF & $0.96 \pm 0.09$ & $0.98 \pm 0.11$ & $0.99 \pm 0.16$ & $0.92 \pm 0.29$ & $0.41 \pm 0.33$ & $0.53 \pm 0.62$ & $0.76 \pm 0.96$ \\ |
| 245 |
> |
\hline |
| 246 |
> |
\hline |
| 247 |
> |
$\mu$+e MC & $483 \pm 19$ & $291 \pm 16$ & $164 \pm 13$ & $47 \pm 7$ & $11 \pm 3$ & $6 \pm 2$ & $3 \pm 2$ \\ |
| 248 |
> |
$\mu$+e Data & $470$ & $292$ & $170$ & $46$ & $10$ & $7$ & $5$ \\ |
| 249 |
> |
\hline |
| 250 |
> |
$\mu$+e Data/MC SF & $0.97 \pm 0.06$ & $1.00 \pm 0.08$ & $1.04 \pm 0.11$ & $0.99 \pm 0.20$ & $0.90 \pm 0.37$ & $1.11 \pm 0.57$ & $1.55 \pm 1.04$ \\ |
| 251 |
|
\hline |
| 252 |
|
\end{tabular}} |
| 253 |
|
\caption{ Yields in \mt\ tail comparing the MC prediction (after |
