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\end{center} |
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\end{table} |
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|
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\clearpage |
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|
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\subsection{Dilepton $P_T$ method} |
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\label{sec:victory} |
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This method is based on a suggestion by V. Pavlunin\cite{ref:victory}, |
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|
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%\noindent {\color{red} For the 11 pb result we have used $K$ from data.} |
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|
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|
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\begin{figure}[bht] |
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\begin{center} |
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\includegraphics[width=0.75\linewidth]{genvictory_Dec13.png} |
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\caption{\label{fig:genvictory}\protect Distributions $P_T(\ell \ell)$ |
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and $P_T(\nu \nu)$ (aka {\it genmet}) |
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in $t\bar{t} \to$ dilepton Monte Carlo at the |
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generator level. Events with $W \to \tau \to \ell$ are not included. |
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No kinematical requirements have been made.} |
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\end{center} |
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\end{figure} |
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|
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|
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There are several effects that spoil the correspondance between \met and |
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$P_T(\ell\ell)$: |
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\begin{itemize} |
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parallel to the $W$ velocity while charged leptons are emitted prefertially |
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anti-parallel. Thus the $P_T(\nu\nu)$ distribution is harder |
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than the $P_T(\ell\ell)$ distribution for top dilepton events. |
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This turns out to be the dominant effect and it is illustrated in |
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Figure~\ref{fig:genvictory}. |
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\item The lepton selections results in $P_T$ and $\eta$ cuts on the individual |
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leptons that have no simple correspondance to the neutrino requirements. |
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\item Similarly, the \met$>$50 GeV cut introduces an asymmetry between leptons and |
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reconstruction level studies, putting the various effects in one at a time. |
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For each configuration, we apply the data-driven method and report as figure |
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of merit the ratio of observed and predicted events in the signal region. |
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The results are summarized in Table~\ref{tab:victorybad}. |
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The figure of merit is calculated as follows |
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\begin{itemize} |
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\item We construct \met/$\sqrt{{\rm sumJetPt}}$ |
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and $P_T(\ell \ell)/\sqrt{{\rm sumJetPt}}$ (rescaled by the factor $K$ defined |
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above) distributions. |
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\item The distributions are constructed using either |
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GEN or RECO, and including or excluding various effects ({\em e.g.:} |
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> |
$t \to W \to \tau \to \ell$). |
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\item In all cases the $N_{jets} \ge 2$ and |
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sumJetPt $>$ 300 GeV requirements are applied. |
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\item ``observed events'' is the integral of the \met/$\sqrt{{\rm sumJetPt}}$ distribution |
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above 8.5. |
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\item ``predicted events'' is the integral of the $P_T(\ell \ell)/\sqrt{{\rm sumJetPt}}$ distribution |
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above 8.5. |
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\end{itemize} |
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The results are summarized in Table~\ref{tab:victorybad}. Distributions corresponding to |
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> |
lines 4 and 5 of Table~\ref{tab:victorybad} are shown in Figure~\ref{fig:victorybad}. |
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|
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\begin{table}[htb] |
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\begin{center} |
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\end{table} |
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|
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|
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+ |
\begin{figure}[bht] |
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\begin{center} |
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\includegraphics[width=0.48\linewidth]{genvictory_sqrtHt_Dec13.png} |
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\includegraphics[width=0.48\linewidth]{victory_Dec13.png} |
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\caption{\label{fig:victorybad}\protect Distributions |
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of MET/$\sqrt{{\rm sumJetPt}}$ (black) and $P_T(\ell \ell)/\sqrt{{\rm sumJetPt}}$ |
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(red) in $t\bar{t} \to$ dilepton Monte Carlo |
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after lepton kinematical cuts, $N_{jets} \ge 2$, and |
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sumJetPt $>$ 300 GeV. The left (right) plot is at the GEN (RECO) level |
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and corresponds to line 4 (5) of Table~\ref{tab:victorybad}.} |
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+ |
\end{center} |
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\end{figure} |
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|
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+ |
|
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+ |
|
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\begin{table}[htb] |
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\begin{center} |
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\caption{\label{tab:victorysyst} |
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An additional source of concern is that the CMS Madgraph $t\bar{t}$ MC does |
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|
not include effects of spin correlations between the two top quarks. |
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We have studied this effect at the generator level using Alpgen. We find |
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< |
that the bias is at the few percent level. |
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> |
that the bias is (at most) at the few percent level. |
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|
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Based on the results of Table~\ref{tab:victorysyst}, we conclude that the |
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naive data-driven background estimate based on $P_T{(\ell\ell)}$ needs to |
