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\section{Introduction}
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In this note we describe a search for new physics in the 2011
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opposite sign isolated dilepton sample ($ee$, $e\mu$, and $\mu\mu$).
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The main sources of high \pt isolated dileptons at CMS are Drell Yan and \ttbar.
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Here we concentrate on dileptons with invariant mass consistent
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with $Z \to ee$ and $Z \to \mu\mu$. A separate search for new physics in the non-\Z
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sample is described in~\cite{ref:GenericOS}.
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We search for new physics in the final state of \Z plus two or more jets plus missing
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transverse energy (MET). We reconstruct the \Z boson
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in its decay to $e^+e^-$ or $\mu^+\mu^-$. Our search regions are defined as
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MET $\ge$ \signalmetl~GeV (loose signal region) and MET $\ge$ \signalmett~GeV
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(tight signal region), and two or more jets. We use data driven techniques to predict the
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standard model background in these search regions.
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Contributions from Drell-Yan production combined with detector mis-measurements that
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produce fake MET are modeled via MET templates based on photon plus jets or QCD events.
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Top pair production backgrounds, as well as other backgrounds for which the lepton
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flavors are uncorrelated such as WW and DY$\rightarrow\tau\tau$, are
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modeled via $e^\pm\mu^\mp$ subtraction.
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As leptonically decaying \Z bosons are a signature that has very little background,
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they provide a clean final state in which to search for new physics.
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Because new physics is expected to be connected to the Standard Model Electroweak sector,
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it is likely that new particles will couple to W and Z bosons.
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For example, in mSUGRA, low $M_{1/2}$ can lead to a significant branching fraction
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for $\chi_2^0 \rightarrow Z \chi_1^0$.
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In addition, we are motivated by the existence of dark matter to search for new physics with MET.
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Enhanced MET is a feature of many new physics scenarios, and R-parity conserving SUSY
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again provides a popular example. The main challenge of this search is therefore to
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understand the tail of the fake MET distribution in \Z plus jets events.
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The basic idea of the MET template method~\cite{ref:templates1}\cite{ref:templates2} is
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to measure the MET distribution in data in a control sample which has no true MET
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and a similar topology to the signal events.
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%Start the qcd vs photon discussion
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Templates can be derived from either a QCD sample (as was done in the original implementation)
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or a photon plus jets sample.
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%In our case, we choose a photon sample with two or more jets as the control sample.
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%Both the control sample and signal sample consist of a well measured object (either a
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%photon or a leptonically decaying $Z$), which recoils against a system of hadronic jets.
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In both cases, the instrumental MET is dominated by mismeasurements of the hadronic system,
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and can be classified by the number of jets in the event and the scalar sum of their transverse
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momenta.
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The prediction is made such that the jet system in the control sample is similar to that of the
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signal sample.
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By using two independent control samples--QCD and photon plus jets--we are able to illustrate
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the robustness of the MET templates method and to cross check the data driven background
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prediction.
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This note is organized as follows.
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In sections \ref{sec:datasets} and \ref{sec:trigSel} we descibe
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the datasets and triggers used, followed by the detailed object definitions (electrons, muons, photons,
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jets, MET) and event selections in sections \ref{sec:evtsel} through \ref{sec:jetsel}.
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We define a preselection and compare data vs. MC yields passing this preselection in
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Section~\ref{sec:yields}.
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We then define the signal regions and show the number of observed events and MC expected
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yields in Section~\ref{sec:sigregion}.
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Section~\ref{sec:templates} introduces the MET template method and discusses its derivation
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in some detail and is followed by a demonstration in Section~\ref{sec:mc}
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that the method works in Monte Carlo.
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Section~\ref{sec:topbkg} introduces the top background estimate based on opposite flavor subtraction,
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and contributions from other backgrounds are discussed in Section~\ref{sec:othBG}.
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Section~\ref{sec:results} shows the results for applying these methods in data.
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We analyze the systematic uncertainties in the background predictions and in signal acceptance
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in Section~\ref{sec:systematics}.
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We then proceed to calculate an upper limit on the non-SM contributions to our signal regions
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in Section~\ref{sec:upperlimit}.
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Efficiencies which can be used to test specific models of new physics are given
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in Section \ref{sec:outreach}.
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Finally, in Section~\ref{sec:models} we calculate upper limits on the quantity \sta\
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assuming efficiencies and uncertainties from sample benchmark SUSY processes.
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