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\section{Background Estimates from Data} |
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\label{sec:datadriven} |
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To look for possible BSM contributions, we define 2 signal regions that preserve about |
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0.1\% of the dilepton $t\bar{t}$ events, by adding requirements of large \MET\ and \Ht: |
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\begin{itemize} |
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\item high \MET\ signal region: \MET $>$ 275~GeV, \Ht $>$ 300~GeV, |
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\item high \Ht\ signal region: \MET $>$ 200~GeV, \Ht $>$ 600~GeV. |
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\end{itemize} |
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For the high \MET\ (high \Ht) signal region, the MC predicts 2.6 (2.5) SM events, |
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dominated by dilepton $t\bar{t}$; the expected LM1 yield is 17 (14) and the |
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expected LM3 yield is 4.3 (4.3). The signal regions are indicated in Fig.~\ref{fig:met_ht}. |
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We use three independent methods to estimate from data the background in the signal region. |
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The first method is a novel technique based on the ABCD method used in our 2010 analysis~\cite{ref:ospaper}, |
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The first method is a novel technique based on the ABCD method, which we used in our 2010 analysis~\cite{ref:ospaper}, |
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and exploits the fact that \HT\ and $y$ are nearly uncorrelated for the $t\bar{t}$ background; |
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this method is referred to as the ABCD' technique. First, we extract the $y$ and \Ht\ distributions |
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$f(y)$ and $g(H_T)$ from data, using events from control regions which are dominated by background. |
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Because $y$ and \Ht\ are weakly-correlated, we can predict the distribution of events in the $y$ vs. \Ht\ plane as: |
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Because $y$ and \Ht\ are weakly-correlated, the distribution of events in the $y$ vs. \Ht\ plane is described by: |
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\begin{equation} |
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\frac{\partial^2 N}{\partial y \partial H_T} = f(y)g(H_T), |
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number of pseudo-events falling in the signal region to the number of pseudo-events |
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falling in a control region defined by the same requirements used to select events |
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to measure $f(y)$ and $g(H_T)$. We then |
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multiply this ratio by the number of \ttbar\ MC events which fall in the control region |
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multiply this ratio by the number events which fall in the control region in data |
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to get the predicted yield, ie. $N_{pred} = R_{S/C} \times N({\rm control})$. |
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To estimate the statistical uncertainty in the predicted background, we smear the bin contents |
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of $f(y)$ and $g(H_T)$ according to their uncertainties. We repeat the prediction 20 times |
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with these smeared distributions, and take the RMS of the deviation from the nominal prediction |
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as the statistical uncertainty. We have studied this technique using toy MC studies based on |
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similar event samples of similar size to the expected yield in data for 1 fb$^{-1}$. |
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event samples of similar size to the expected yield in data for 1 fb$^{-1}$. |
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Based on these studies we correct the predicted backgrounds yields by factors of 1.2 $\pm$ 0.5 |
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(1.0 $\pm$ 0.5) for the high \MET\ (high \Ht) signal region. |
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reliably accounted for. We then use the observed |
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$\pt(\ell\ell)$ distribution to model the $\pt(\nu\nu)$ distribution, |
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which is identified with \MET. Thus, we use the number of observed |
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events with $\HT > 300\GeV$ and $\pt(\ell\ell) > 275\GeV^{1/2}$ |
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events with $\HT > 300\GeV$ and $\pt(\ell\ell) > 275\GeV$ |
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($\HT > 600\GeV$ and $\pt(\ell\ell) > 200\GeV^{1/2}$ ) |
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to predict the number of background events with |
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$\HT > 300\GeV$ and $\MET = > 275\GeV^{1/2}$ ($\HT > 600\GeV$ and $\MET = > 200\GeV^{1/2}$). |
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$\HT > 300\GeV$ and $\MET > 275\GeV$ ($\HT > 600\GeV$ and $\MET > 200\GeV$). |
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In practice, two corrections must be applied to this prediction, as described below. |
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% |
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rescale the prediction by a factor equal to the inverse of the |
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fraction of events passing the preselection which also satisfy the |
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requirement $\pt(\ell\ell) > 50\GeVc$. |
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For the \Ht $>$ 300 GeV requirement corresponding to the high \MET\ signal region, |
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For the \Ht\ $>$ 300 GeV requirement corresponding to the high \MET\ signal region, |
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we determine this correction from data and find $K_{50}=1.5 \pm 0.3$. |
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For the \Ht $>$ 600 GeV requirement corresponding to the high \Ht\ signal region, |
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For the \Ht\ $>$ 600 GeV requirement corresponding to the high \Ht\ signal region, |
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we do not have enough events in data to determine this correction with statistical |
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precisions, so we instead extract it from MC and find $K_{50}=1.3 \pm 0.2$. |
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precision, so we instead extract it from MC and find $K_{50}=1.3 \pm 0.2$. |
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The second correction ($K_C$) is associated with the known polarization of the $W$, which |
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introduces a difference between the $\pt(\ell\ell)$ and $\pt(\nu\nu)$ |
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distributions. The correction $K_C$ also takes into account detector effects such as the hadronic energy |
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in the extraction of $K_C$ and the 5\% uncertainty in the hadronic energy scale~\cite{ref:jes}. |
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Our third background estimation method is based on the fact that many models of new physics |
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produce an excess of SF with respect to OF lepton pairs. In SUSY, such an excess may produced |
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produce an excess of SF with respect to OF lepton pairs. In SUSY, such an excess may be produced |
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in the decay $\chi_2^0 \to \chi_1^0 \ell^+\ell^-$ or in the decay of $Z$ bosons produced in |
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the cascade decays of heavy, colored objects. In contrast, for the \ttbar\ background the |
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rates of SF and OF lepton pairs are the same, as is also the case for other SM backgrounds |
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\Delta = R_{\mu e}N(ee) + \frac{1}{R_{\mu e}}N(\mu\mu) - N(e\mu), |
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\end{equation} |
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where $R_{\mu e} = 1.13 \pm 0.05$ is the ratio of muon to electron selection efficiencies. |
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This quantity is evaluated by taking the square root of the ratio of the number of observed |
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$Z \to \mu^+\mu^-$ to $Z \to e^+e^-$ events, in the mass range 76-106 GeV with no jets or |
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where $R_{\mu e} = 1.13 \pm 0.05$ is the ratio of muon to electron selection efficiencies, |
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evaluated by taking the square root of the ratio of the number of |
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$Z \to \mu^+\mu^-$ to $Z \to e^+e^-$ events in data, in the mass range 76-106 GeV with no jets or |
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\met\ requirements. The quantity $\Delta$ is predicted to be 0 for processes with |
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uncorrelated lepton flavors. In order for this technique to work, the kinematic selection |
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applied to events in all dilepton flavor channels must be the same, which is not the case |
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for our default selection because the $Z$ mass veto is applied only to same-flavor channels.Therefore when applying the OF subtraction technique we also apply the $Z$ mass veto also |
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for our default selection because the $Z$ mass veto is applied only to same-flavor channels. |
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Therefore when applying the OF subtraction technique we also apply the $Z$ mass veto also |
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to the $e\mu$ channel. |
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All background estimation methods based on data are in principle subject to signal contamination |
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in the different control regions for the two methods. |
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For example, in the extreme case of a |
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BSM signal with identical distributions of $\pt(\ell \ell)$ and \MET, an excess of events might be seen |
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in the ABCD method but not in the $\pt(\ell \ell)$ method. |
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in the ABCD' method but not in the $\pt(\ell \ell)$ method. |
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