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\section{Non $t\bar{t}$ Backgrounds}
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\label{sec:othBG}
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Backgrounds from divector bosons and single top
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can be reliably estimated from Monte Carlo.
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They are negligible compared to $t\bar{t}$.
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Backgrounds from Drell Yan are also expected
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to be negligible from MC. However one always
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worries about the modeling of tails of the \met.
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In the context of other dilepton analyses we
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have developed a data driven method to estimate
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the number of Drell Yan events\cite{ref:dy}.
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The method is based on counting the number of
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$Z$ candidates passing the full selection, and
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then scaling by the expected ratio of Drell Yan
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events outside vs. inside the $Z$ mass
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window.\footnote{A correction based on $e\mu$ events
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is also applied.} This ratio is called $R_{out/in}$
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and is obtained from Monte Carlo.
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To estimate the Drell-Yan contribution in the four $ABCD$
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regions, we count the numbers of $Z \to ee$ and $Z \to \mu\mu$
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events falling in each region, we subtract off the number
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of $e\mu$ events with $76 < M(e\mu) < 106$ GeV, and
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we multiply the result by $R_{out/in}$ from Monte Carlo.
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The results are summarized in Table~\ref{tab:ABCD-DY}.
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\begin{table}[hbt]
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\begin{center}
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\caption{\label{tab:ABCD-DY} Drell-Yan estimations
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in the four
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regions of Figure~\ref{fig:abcdData}. The yields are
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for dileptons with invariant mass consistent with the $Z$.
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The factor
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$R_{out/in}$ is from MC. All uncertainties
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are statistical only.}
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\begin{tabular}{|l|c|c|c||c|}
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\hline
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Region & $N(ee)+N(\mu\mu)$ & $N(e\mu)$ & $R_{out/in}$ & Estimated DY BG \\
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\hline
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$A$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$B$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$C$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$D$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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\hline
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\end{tabular}
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\end{center}
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\end{table}
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%When find no dilepton events with invariant mass
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%consistent with the $Z$ in the signal region.
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%Using the value of 0.1 for the ratio described above, this
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%means that the Drell Yan background in our signal
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%region is $< 0.23\%$ events at the 90\% confidence level.
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%{\color{red} (If we find 1 event this will need to be adjusted)}.
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As discussed in Section~\ref{sec:victory}, residual Drell-Yan
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events can have a significant effect on the data driven background
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prediction based on $P_T(\ell\ell)$. This is taken into account,
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based on MC expectations,
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by the $K_{\rm{fudge}}$ factor describes in that Section.
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As a cross-check, we use the same Drell Yan background
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estimation method described above to estimated the
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number of DY events in the regions $A'B'C'D'$.
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The region $A'$ is defined in the same way as the region $A$
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except that the $\met/\sqrt{\rm SumJetPt}$ requirement is
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replaced by a $P_T(\ell\ell)/\sqrt{\rm SumJetPt}$ requirement.
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The regions B',
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C', and D' are defined in a similar way. The results are
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summarized in Table~\ref{tab:ABCD-DYptll}.
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\begin{table}[hbt]
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\begin{center}
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\caption{\label{tab:ABCD-DYptll} Drell-Yan estimations
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in the four
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regions $A'B'C'D'$ defined in the text. The yields are
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for dileptons with invariant mass consistent with the $Z$.
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The factor
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$R_{out/in}$ is from MC. All uncertainties
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are statistical only.}
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\begin{tabular}{|l|c|c|c||c|}
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\hline
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Region & $N(ee)+N(\mu\mu)$ & $N(e\mu)$ & $R_{out/in}$ & Estimated DY BG \\
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\hline
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$A'$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$B'$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$C'$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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$D'$ & xx & xx & xx$\pm$xx & xx$\pm$xx \\
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\hline
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\end{tabular}
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\end{center}
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\end{table}
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Finally, we can use the ``Fake Rate'' method\cite{ref:FR}
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to predict
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the number of events with one fake lepton. We select
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events where one of the leptons passes the full selection and
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the other one fails the full selection but passes the
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``Fakeable Object'' selection of
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Reference~\cite{ref:FR}.\footnote{For electrons we use
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the V3 fakeable object definition to avoid complications
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associated with electron ID cuts applied in the trigger.}
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We then weight each event passing the full selection
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by FR/(1-FR) where FR is the ``fake rate'' for the
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fakeable object. {\color{red} The results are...} |
