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\section{Lepton Selection}\label{section:Leptons} |
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\section{Lepton Selection}\label{sec:Leptons} |
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%++++++++++++++++++++++++++++++++++++++++++++++++++ |
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%++++++++++++++++++++++++++++++++++++++++++++++++++ |
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%__________________________________________________ |
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\subsubsection{Offline Selection} |
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\subsubsection{Offline Muon Selection}\label{sec:muOffline} |
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%__________________________________________________ |
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We select offline muon candidates that satisfy the critria in Tables~\ref{tab:muonID} and~\ref{tab:muonIso}. The primary difference between these criteria and those of~\cite{baseline} are our inclusion of Tracker muons, which provide us with a high-efficiency low-$p_{T}$ reconstruction path. We also introduce additional quality requirements intended to reduce non-prompt backgrounds and we impose $\eta/p_{T}$ dependent, per-muon PF isolation requirements. |
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We select offline muon candidates that satisfy the requirements given in Tables~\ref{tab:muonID} and~\ref{tab:muonIso}. The main difference between these criteria and those of~\cite{baseline} is our inclusion of Tracker muons, which provide a high-efficiency reconstruction path at low-$p_{T}$. We also introduce additional quality requirements designed to reduce non-prompt backgrounds and we impose $\eta/p_{T}$ dependent, per-muon PF relative isolation. |
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\begin{table}[tbh] |
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\hline |
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\multicolumn{2}{}{~\\} \\ |
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\multicolumn{2}{}{~} \\ |
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\hline |
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\multicolumn{2}{c}{Tracker Muons} \\ |
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\hline |
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Qualtity Bits & LastStationTight \\ |
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Quality Bits & LastStationTight \\ |
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\hline |
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\multicolumn{2}{}{~\\} \\ |
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\multicolumn{2}{}{~} \\ |
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\hline |
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\multicolumn{2}{c}{Global Muons} \\ |
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\hline |
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\end{table} |
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% CMS AN-2011/097 TP |
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We measure the efficiency of this selection using samples of $Z \rightarrow \mu\mu$ events and the ``Tag \& Probe'' technique~\cite{TP}. The $\mathcal{L} = 2.1\rm~fb{-1}$ dataset contains a sufficient number of $Z$ events for us to obtain selection efficiencies for muons below $10\rm~GeV$, thus we do not utilize separate samples of low-mass resonances for this region. We require events containing at least one muon candidate that passes the full set of muon identifiation criteria (the tag) and at least one additional reconstructed Global or Tracker muon candidate (the probe). The sample is split according to whether the probe passes of fails our selection. We determine efficiency in MC by simply counting the number of events that pass or fail the selection in bins of $p_{T}$ and $\eta$. Efficiency is extracted in data by fitting with MC signal shapes and empirical functional hypotheses for the background. Figures~\ref{fig:muTPhighpt} and~\ref{fig:muTPlowpt} respectively show fits results in the central region for high and low $p_{T}$ bins. The complete set of fit results are included in Appendix~\ref{app:mufits}. |
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% |
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We measure the efficiency of this selection using samples of $Z \rightarrow \mu\mu$ events and the ``Tag \& Probe'' technique~\cite{TP}. The $\mathcal{L} = 4.7\rm~fb^{-1}$ dataset contains a sufficient number of $Z$ events to obtain selection efficiencies for muons below $10\rm~GeV$ -- we do not utilize separate samples of low-mass resonances for this $p_{T}$ region. We require events that contain at least one muon candidate passing the full set of muon identification criteria (the tag) and at least one additional reconstructed Global or Tracker muon candidate (the probe). The sample is split according to whether the probe passes of fails our selection. We determine efficiency in MC by simply counting the number of events that pass or fail the selection in bins of $p_{T}$ and $\eta$. Efficiency is extracted in data by fitting with MC signal shapes and empirical function for the background. Figures~\ref{fig:muTPhighpt} and~\ref{fig:muTPlowpt} respectively show fits results in the central region for high and low $p_{T}$ bins. |
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\begin{figure}[htb] |
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\end{figure} |
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We divide the $p_{T}/\eta$ binned data efficiencies with correpsonding values from MC to determine data/MC efficiency scale factors, $f_{ID,Iso}$. We use these factors to weight selected muons in our MC samples, as discussed in Sections~\ref{sec:} and~\ref{sec:}. Figure~\ref{fig:muEff} shows $f_{ID,Iso}$ for the central and forward regions as a function of $p_{T}$. Table~\ref{tab:musf} lists values for $f_{ID,Iso}$ in our $p_{T}/\eta$ bins. |
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We divide the $p_{T}/\eta$-binned efficiencies from data with corresponding values from MC to determine data/MC efficiency scale factors, $f_{ID,Iso}$. We use these factors to weight selected muons in our MC samples, as is discussed in Sections~\ref{sec:Signal}. Figure~\ref{fig:muEff} shows $f_{ID,Iso}$ for the central and forward regions as a function of $p_{T}$. Table~\ref{tab:musf} lists values for $f_{ID,Iso}$ in our $p_{T}/\eta$ bins. |
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\begin{figure}[htb] |
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\begin{center} |
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\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta0.png} |
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\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta1.png} |
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\caption{$\gamma_d$ Branching Ratios.}\label{fig:muEff} |
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\caption{Offline Muon Efficiency Scale Factors.}\label{fig:muEff} |
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\end{center} |
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\end{figure} |
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$100 < p_T < 7000$ & $0.9978 \pm 0.0027$ & $1.0049 \pm 0.0083$ \\ |
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\hline |
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\end{tabular} |
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\caption{Write some stuff} |
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\label{tab:mytable} |
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\caption{Write some stuff}\label{tab:musf} |
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\end{center} |
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\end{table} |
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%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
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Identification and isolation efficiencies for non-prompt and instrumental muon backgrounds are also evaluated with data. We defer further discussion of this to Section~{sec:} |
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Identification and isolation efficiencies for non-prompt and instrumental muon backgrounds are also evaluated with data. We defer discussion of this to Section~\ref{sec:BG} |
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%csidetermine a background efficiency ({\it i.e} a ``fakerate'' in the terminology of Section~\ref{sec:}) with respect to objects passing the loose subset of muon indentification criteria listed in Table~\ref{tab:muFO}. We calculate the fakerate using data collected with a single muon trigger. We require a jet of at least $30~\rm{Gev}$ with $\Delta R(\eta,\phi) > 1.5$ from the muon candidate in order to enrich this sample in background. Contributions from W, Z and low-mass resonances are reduced by additionally requiring events that contain only one muon denominator object above $10\rm~GeV$, $MET < 20 ~\rm{GeV}$ and $m_{T} < 30~\rm{GeV}$. |
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%__________________________________________________ |
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\subsubsection{Online Selection}\label{muOnline} |
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\subsubsection{Online Muon Selection}\label{sec:muOnline} |
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%__________________________________________________ |
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We measure $p_{T}/\eta$ binned per-leg efficiencies for the \verb|HLT_DoubleMu_7| and \verb|HLT_Mu_13_8| triggers also using Tag \& Probe. The efficiencies are calculated with respect to the muon candidates that pass the offline requirements described above. We do not use the emulation of these triggers in MC and instead correct the simulation with the absolute efficiency measured in data. Backgrounds after offline selection are so small that we can determine trigger efficiency by simply counting. Tables~\ref{tab:trigEffMu7}-\ref{tab:trigEffMu13_8_trailing} provides the per-leg efficiencies we determine in our various $p_{T}/eta$ bins. |
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We use Tag \& Probe to also measure $p_{T}/\eta$-binned per-leg efficiencies for the \verb|HLT_DoubleMu_7| and \verb|HLT_Mu_13_8| triggers. The trigger efficiencies are calculated with respect to muon candidates that pass the offline requirements described in Section~\ref{sec:muOnline}. We do not use the emulation of these triggers in MC and instead correct the simulation with the absolute efficiencies measured in data. Backgrounds after offline selection are small, so trigger efficiency is determined by simply counting events. Tables~\ref{tab:trigEffMu7}-\ref{tab:trigEffMu13_8_trailing} provide the per-leg efficiencies for our various $p_{T}/eta$ bins. |
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% figs/mueff/Run2011A_HLT_DoubleMu7/default/extra/dat_eff_table.tex |
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%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
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$100 < p_T < 7000$ & $0.9801 \pm 0.0189$ & $0.9405 \pm 0.0383$ & $0.9490 \pm 0.0330$ & $1.0000 \pm 0.2313$ \\ |
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\hline |
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\end{tabular} |
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\caption{Write some stuff} |
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\label{tab:mytable} |
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\caption{Write some stuff}\label{tab:trigEffMu7} |
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\end{center} |
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\end{table} |
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\hline |
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\caption{Write some stuff} |
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\label{tab:mytable} |
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\label{tab:trigEffMu13_8_leading} |
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\end{center} |
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\end{table} |
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\hline |
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\caption{Write some stuff} |
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\label{tab:mytable} |
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\label{tab:trigEffMu13_8_trailing} |
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\end{center} |
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\end{table} |
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%__________________________________________________ |
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\subsection{Offline Selection} |
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We select electron candidates for the analysis using a multivariate (MV) technique. Our method was developed in parallel with an MV-based electron ID scheme for the WW analysis~\cite{ref:si}. The two methods are equivalent, modulo small differences in implementation that address the relative severity of ``fake'' electron backgrounds in the respectve analyses. |
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We select electron candidates for the analysis using a multivariate (MV) technique. Our method was developed in concert with an MV-based electron ID scheme for the WW analysis~\cite{si}. The two methods are equivalent, modulo small differences in implementation that address the relative severity of ``fake'' electron backgrounds in the respective analyses. |
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We utilize a TMVA Boosted Decision Tree (BDT) for MV identification. The BDT is trained with separate samples of candidate objects that are enriched in either fake or real electrons. Candidates are defined as reconstructed electrons that pass the minimal set of selection criteria given in Table~\ref{tab:eleFO}. We construct a signal training sample from pairs of candidates in the DoubleElectron dataset with $|m_{\ell\ell}| < 15~\rm GeV$. Canidates in the signal training sample are required to have $Iso_{comb} < 0.1$ to reduce combinatoric background. Candidates for the background training sample are selected from events that pass a single-electron trigger. As with muons, we require an $\Delta R(\eta,\phi)$ $p_{T}>35~\rm GeV$ jet and reject events with $\rm MET > 20~GeV$, $\rm m_{T} > 30~GeV$ or containing more than one canididate. In addition, we veto conversions to suppress real electron contamination in the background training sample. |
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We utilize a TMVA Boosted Decision Tree (BDT) for MV identification. The BDT is trained with separate samples of candidate objects that are enriched in either fake or real electrons. Candidates are defined as reconstructed electrons that pass the minimal set of selection criteria listed in Table~\ref{tab:eleFO}. We construct a signal training sample from pairs of candidates in the DoubleElectron dataset with $|m_{\ell\ell} - M_{Z}| < 15~\rm GeV$. Candidates in the background training sample are selected from events that pass a single-electron trigger. We require a $\Delta R(\eta,\phi) >1~\rm$ jet and reject events with $\rm MET > 20~GeV$, or containing more than one candidate. We also veto conversions to suppress real electron contamination. |
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%------------------------------------------------- |
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\begin{table}[tbh] |
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\begin{center} |
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\begin{tabular}{c|c|c} |
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\begin{tabular}{c|c} |
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{\bf Quantity} & {\bf Requirement}\\ |
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\hline |
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\multicolumn{3}{c}{Relative PF Isolation} \\ |
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\hline |
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$\rm p_{T}$ & $|\eta|$ & $\rm pfIso03/p_{T}$ \\ |
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\hline |
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$> 20$ & $< 1.48$ & $ < 0.13 $ \\ |
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$> 20$ & $> 1.48$ & $ < 0.09 $ \\ |
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$< 20$ & $< 1.48$ & $ < 0.06 $ \\ |
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$< 20$ & $> 1.48$ & $ < 0.05 $ \\ |
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$|dz|$ & $< 0.1\rm~cm$ \\ |
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$H/E$ & $< 0.12(0.1) EB(EE)$ \\ |
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$iso_{trk}$ & $<0.3$ \\ |
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$iso_{em}$ & $<0.3$ \\ |
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$iso_{had}$ & $<0.3$ \\ |
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\caption{Electron Candidate Defintion.\label{tab:eleFO}} |
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\caption{Electron Candidate Definition.\label{tab:eleFO}} |
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\end{center} |
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\end{table} |
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%------------------------------------------------- |
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MV discrimination is performed using the following variables : |
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\begin{itemize} |
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\item a |
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\item b |
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\item c |
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\item $\sigma_{i\eta i\eta}$ |
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\item $\sigma_{i\phi i\phi}$ |
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\item $\Delta\eta_{in}$ |
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\item $\Delta\phi_{in}$ |
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\item fBrem |
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\item nBrem |
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\item $E/P$ |
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\item D0 |
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\item $E_{seed}/P_{out}$ |
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\item $E_{seed}/P_{in}$ |
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\item $1/E - 1/P$ |
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\end{itemize} |
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Cuts on these guys? Show correlation plot to motivate BDT? |
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{\bf Cuts on these guys? Show correlation plot to motivate BDT?} |
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We train and validate the BDT using statistically independet subsets of events from the trainging samples described above. Training and testing is performed separately for six $\eta/p_{T}$ bins. A cut on the resulting BDT discrminant translates to a specific combination of signal and background efficiency. The locus of signal/background efficiences defines the discrimination performance ({\it i.e:} ROC) curves shown in Figure~\ref{fig:ROC}. |
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We train and validate the BDT using statistically independent subsets of events from the samples described above. Training and testing is performed separately for six $\eta/p_{T}$ bins. A cut on the resulting BDT discriminant translates to a specific combination of signal and background efficiency. The locus of signal/background efficiencies yields the performance ({\it i.e:} ROC) curves shown in Figure~\ref{fig:ROC}. |
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%------------------------------------------------- |
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\begin{figure}[tbp] |
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\begin{center} |
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\includegraphics[width=0.4\linewidth]{figs/br.png} |
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\includegraphics[width=0.4\linewidth]{figs/br.png} |
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\caption{$\gamma_d$ Branching Ratios.\label{fig:BRs} } |
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\includegraphics[width=0.4\linewidth]{figs/roc-s0_pt1.png} |
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\includegraphics[width=0.4\linewidth]{figs/roc-s2_pt0.png} |
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\caption{MVA Electron ID Performance. \label{fig:ROC} } |
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\end{center} |
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\end{figure} |
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%------------------------------------------------- |
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The plots in Figure~\ref{fig:ROC} include efficiency points corresponding to the ``Cuts in Categories'' (CIC) loose, medium and tight working points defined in~\cite{ref:CIC}. BDT and CIC performances are comparable in the high $p_{T}$ bins, while the BDT clearly outperforms CIC at low $p_{T}$. We define a set of loose, medium and tight BDT working points for the analysis by stipulating a background effiency equivalent to that of the corresponding CIC point. BDT and CIC signal efficiences for the various working points are compared in Table~\ref{tab:WPs}. |
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The plots in Figure~\ref{fig:ROC} include efficiency points corresponding to the ``Cuts in Categories'' (CIC) loose, medium and tight working points defined in~\cite{CIC}. BDT and CIC performances are comparable in the high $p_{T}$ bins, whereas the BDT outperforms CIC at low $p_{T}$. We define a set of loose, medium and tight BDT working points for this analysis by stipulating background efficiencies equivalent to those of the corresponding CIC working points. |
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%------------------------------------------------- |
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\begin{table}[tbh] |
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\begin{center} |
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\begin{tabular}{|c|c|c|} |
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\hline |
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\multicolumn{3}{|c|}{Relative PF Isolation} \\ |
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\hline |
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$\rm p_{T}$ & $|\eta|$ & $\rm pfIso03/p_{T}$ \\ |
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\hline |
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$> 20$ & $< 1.48$ & $ < 0.13 $ \\ |
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$> 20$ & $> 1.48$ & $ < 0.09 $ \\ |
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$< 20$ & $< 1.48$ & $ < 0.06 $ \\ |
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$< 20$ & $> 1.48$ & $ < 0.05 $ \\ |
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\hline |
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\end{tabular} |
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\caption{Working Points and Efficiencies.\label{tab:WPs}} |
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\end{center} |
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\end{table} |
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%------------------------------------------------- |
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%% BDT and CIC signal efficiencies for the various working points are compared in Table~\ref{tab:WPs}. |
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|
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%% %------------------------------------------------- |
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%% \begin{table}[tbh] |
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%% \begin{center} |
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%% \begin{tabular}{c|c|c} |
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%% $\epsilon_{B}$ & $\epsilon_{S}(CIC)$ & $\epsilon_{S}(BDT)$ \\ |
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%% \hline |
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%% $X$ & $Y$ & $Z$ \\ |
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%% $X$ & $Y$ & $Z$ \\ |
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%% $X$ & $Y$ & $Z$ \\ |
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%% $X$ & $Y$ & $Z$ \\ |
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%% \hline |
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%% \end{tabular} |
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%% \caption{Working Points and Efficiencies.\label{tab:WPs}} |
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%% \end{center} |
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%% \end{table} |
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%% %------------------------------------------------- |
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The efficiencies shown above are determined with respect to the candidate definition in Table~\ref{tab:}. While these efficiencies are useful for performance comparisons, efficiencies for the analysis must be taken with respect to reconstructed GSF electron electrons. As with muons, we calculate electron identification/isolation efficiencies for the anlaysis using Tag \& Probe. Figures~\ref{fig:} and ~\ref{fig:} shows fit results for our tight MV selection in the central region. The complete set of offline selection fits from Tag \& Probe are included in Appendix~\ref{app:}. |
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The efficiencies shown in Figure~\ref{fig:ROC} are determined with respect to the candidate definition in Table~\ref{tab:eleFO}. While these values are useful for performance comparison, efficiencies for the analysis must be taken with respect to reconstructed GSF electrons. As with muons, we calculate electron identification/isolation efficiencies for the analysis using Tag \& Probe. Figures~\ref{fig:eleTPmediumhighpt} and ~\ref{fig:eleTPmediumlowpt} (~\ref{fig:eleTPloosehighpt} and ~\ref{fig:eleTPlooselowpt}) show fit results for our medium (loose) MV selection in the central region. %The complete set of offline selection fits from Tag \& Probe are included in Appendix~\ref{app:}. |
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%------------------------------------------------- |
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\begin{figure}[htb] |
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\begin{center} |
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\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/passetapt_6.png} |
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\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/failetapt_6.png} |
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\caption{Tag \& Probe fit results for tight offline selection for high-$p_{T}$ electrons in the barrel.\label{fig:ekeTPhighpt} } |
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\caption{Tag \& Probe fit results for medium offline selection for high-$p_{T}$ electrons in the barrel. {\bf FIX! Currently pictures are for tight} } |
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\label{fig:eleTPmediumhighpt} |
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\end{center} |
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\end{figure} |
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%------------------------------------------------- |
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\begin{center} |
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\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/passetapt_0.png} |
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\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/failetapt_0.png} |
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\caption{Tag \& Probe fit results for tight offline selection for low-$p_{T}$ electrons in the barrel.\label{fig:eleTPlowpt} } |
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\caption{Tag \& Probe fit results for medium offline selection for low-$p_{T}$ electrons in the barrel. {\bf FIX! Currently pictures are for tight} } |
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\label{fig:eleTPmediumlowpt} |
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\end{center} |
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\end{figure} |
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%------------------------------------------------- |
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We divide the binned data efficiences with results from MC to obtain offline efficiency scale factors, $f_{ID,Iso}$ in Tables~\ref{tab:eleSFtight}-~\ref{tab:eleSFloose}. Figures~\ref{fig:eleSFtight}-~\ref{fig:eleSFloose} shows plots of the $f_{ID,Iso}$ as a function of $p_{T}$ for the central and forward regions. |
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|
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%figs/eleeff/Run2011A_EleWPEffTP-tight/default/extra/sf_table.tex |
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%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
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\begin{table}[!ht] |
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%------------------------------------------------- |
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\begin{figure}[htb] |
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\begin{center} |
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\begin{tabular}{c|c|c} |
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\hline & $0 < |\eta| < 1.5$ & $1.5 < |\eta| < 2.5$ \\ |
| 313 |
< |
\hline |
| 314 |
< |
$ 7 < p_T < 10$ & $1.3279 \pm 0.1095$ & $1.0983 \pm 0.0495$ \\ |
| 314 |
< |
$ 10 < p_T < 15$ & $0.5121 \pm 0.0067$ & $1.8054 \pm 0.0101$ \\ |
| 315 |
< |
$ 15 < p_T < 20$ & $1.0371 \pm 0.0090$ & $1.0791 \pm 0.0110$ \\ |
| 316 |
< |
$ 20 < p_T < 30$ & $0.9782 \pm 0.0013$ & $1.0107 \pm 0.0022$ \\ |
| 317 |
< |
$ 30 < p_T < 40$ & $0.9973 \pm 0.0002$ & $1.0106 \pm 0.0047$ \\ |
| 318 |
< |
$ 40 < p_T < 50$ & $0.9947 \pm 0.0002$ & $1.0020 \pm 0.0027$ \\ |
| 319 |
< |
$ 50 < p_T < 100$ & $0.9841 \pm 0.0005$ & $1.0003 \pm 0.0007$ \\ |
| 320 |
< |
$100 < p_T < 7000$ & $1.0040 \pm 0.0041$ & $1.0114 \pm 0.0297$ \\ |
| 321 |
< |
\hline |
| 322 |
< |
\end{tabular} |
| 323 |
< |
\caption{Write some stuff} |
| 324 |
< |
\label{tab:eleSFtight} |
| 311 |
> |
\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/passetapt_6.png} |
| 312 |
> |
\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/failetapt_6.png} |
| 313 |
> |
\caption{Tag \& Probe fit results for loose offline selection for high-$p_{T}$ electrons in the barrel. {\bf FIX! Currently pictures are for tight} } |
| 314 |
> |
\label{fig:eleTPloosehighpt} |
| 315 |
|
\end{center} |
| 316 |
< |
\end{table} |
| 317 |
< |
|
| 328 |
< |
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
| 329 |
< |
|
| 316 |
> |
\end{figure} |
| 317 |
> |
%------------------------------------------------- |
| 318 |
|
%------------------------------------------------- |
| 319 |
|
\begin{figure}[htb] |
| 320 |
|
\begin{center} |
| 321 |
< |
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta0.png} |
| 322 |
< |
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta1.png} |
| 323 |
< |
\caption{SF for ele tight. Cureently muon plots ...}\label{fig:eleSFtight} |
| 321 |
> |
\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/passetapt_0.png} |
| 322 |
> |
\includegraphics[width=0.5\linewidth]{figs/eleeff/Run2011A_EleWPEffTP-tight/default/plots/failetapt_0.png} |
| 323 |
> |
\caption{Tag \& Probe fit results for loose offline selection for low-$p_{T}$ electrons in the barrel. {\bf FIX! Currently pictures are for tight} } |
| 324 |
> |
\label{fig:eleTPlooselowpt} |
| 325 |
|
\end{center} |
| 326 |
|
\end{figure} |
| 327 |
|
%------------------------------------------------- |
| 328 |
|
|
| 329 |
+ |
We divide the binned data efficiencies with corresponding values from MC to obtain offline efficiency scale factors, $f_{ID,Iso}$. Tables~\ref{tab:eleSFmedium}-~\ref{tab:eleSFloose} list these factors for the medium and loose offline selections. Figures~\ref{fig:eleSFmedium} and ~\ref{fig:eleSFloose} plot the $f_{ID,Iso}$ as functions of $p_{T}$ for the central and forward regions. |
| 330 |
+ |
|
| 331 |
+ |
|
| 332 |
|
%eleeff/Run2011A_EleWPEffTP-medium/default/extra/sf_table.tex |
| 333 |
|
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
| 334 |
|
\begin{table}[!ht] |
| 346 |
|
$100 < p_T < 7000$ & $0.9828 \pm 0.0028$ & $1.0144 \pm 0.0024$ \\ |
| 347 |
|
\hline |
| 348 |
|
\end{tabular} |
| 349 |
< |
\caption{Write some stuff} |
| 350 |
< |
\label{tab:mytable} |
| 349 |
> |
\caption{MVA Medium ID scale factors.} |
| 350 |
> |
\label{tab:eleSFmedium} |
| 351 |
|
\end{center} |
| 352 |
|
\end{table} |
| 353 |
|
|
| 358 |
|
\begin{center} |
| 359 |
|
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta0.png} |
| 360 |
|
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta1.png} |
| 361 |
< |
\caption{SF for ele medium. Cureently muon plots ...}\label{fig:eleSFmedium} |
| 361 |
> |
\caption{SF for ele medium. {\bf FIX! Currently muon plots ...}} |
| 362 |
> |
\label{fig:eleSFmedium} |
| 363 |
|
\end{center} |
| 364 |
|
\end{figure} |
| 365 |
|
%------------------------------------------------- |
| 381 |
|
$100 < p_T < 7000$ & $0.9946 \pm 0.0045$ & $1.0134 \pm 0.0067$ \\ |
| 382 |
|
\hline |
| 383 |
|
\end{tabular} |
| 384 |
< |
\caption{Write some stuff} |
| 385 |
< |
\label{tab:mytable} |
| 384 |
> |
\caption{MVA Loose ID Efficiency Scale Factors.} |
| 385 |
> |
\label{tab:eleSFloose} |
| 386 |
|
\end{center} |
| 387 |
|
\end{table} |
| 388 |
|
|
| 393 |
|
\begin{center} |
| 394 |
|
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta0.png} |
| 395 |
|
\includegraphics[width=0.4\linewidth]{figs/mueff/Run2011A_MuonWPEffTP/default/extra/scalept_eta1.png} |
| 396 |
< |
\caption{SF for ele loose. Cureently muon plots ...}\label{fig:eleSFloose} |
| 396 |
> |
\caption{SF for ele loose. {\bf FIX! Currently muon plots ...}} |
| 397 |
> |
\label{fig:eleSFloose} |
| 398 |
|
\end{center} |
| 399 |
|
\end{figure} |
| 400 |
|
%------------------------------------------------- |
| 401 |
|
|
| 402 |
< |
Identification and isolation efficiencies for non-prompt and instrumental electron backgrounds are also evaluated with data. We defer further discussion of this to Section~{sec:} |
| 402 |
> |
Identification and isolation efficiencies for non-prompt and instrumental electron backgrounds are also evaluated with data. We defer discussion of this to Section~\ref{sec:BG}. |
| 403 |
|
|
| 404 |
|
%__________________________________________________ |
| 405 |
|
\subsubsection{Online Selection}\label{sec:eleOnline} |
| 406 |
|
%__________________________________________________ |
| 407 |
< |
Per-leg efficiencies for the various electron trigger efficiencies are calculated in the same manner as was described in Section~\ref{sec:muOnline}. Table~\ref{tab:eleTPtrig} lists the luminosity-averaged efficiencies defined with respect to selected offline electrons in bins of $p_{T}$ and $\eta$. |
| 407 |
> |
Per-leg efficiencies for the various electron triggers are calculated in the same manner as was described in Section~\ref{sec:muOnline}. Table~\ref{tab:eleTPtrigLeading} lists the luminosity-averaged efficiencies for leading and trailing trigger legs defined with respect to selected offline electrons. |
| 408 |
|
|
| 409 |
|
%figs/mueff/Run2011A_HLT_Mu13_Mu8_trailing/default/extra/dat_eff_table.tex |
| 410 |
|
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
| 423 |
|
$100 < p_T < 7000$ & $0.9662 \pm 0.0054$ & $0.9529 \pm 0.0096$ & $0.9443 \pm 0.0083$ & $0.9577 \pm 0.0394$ \\ |
| 424 |
|
\hline |
| 425 |
|
\end{tabular} |
| 426 |
< |
\caption{Write some stuff} |
| 427 |
< |
\label{tab:mytable} |
| 426 |
> |
\caption{Trigger Efficiency for the Leading Leg of the (luminosity-average) Double Electron trigger. {\bf FIX! Get the correct numbers in here.} } |
| 427 |
> |
\label{tab:eleTPtrigLeading} |
| 428 |
|
\end{center} |
| 429 |
|
\end{table} |
| 430 |
|
|
| 447 |
|
$100 < p_T < 7000$ & $0.9662 \pm 0.0054$ & $0.9529 \pm 0.0096$ & $0.9443 \pm 0.0083$ & $0.9577 \pm 0.0394$ \\ |
| 448 |
|
\hline |
| 449 |
|
\end{tabular} |
| 450 |
< |
\caption{Write some stuff} |
| 451 |
< |
\label{tab:mytable} |
| 450 |
> |
\caption{Trigger Efficiency for the Trailing Leg of the (luminosity-average) Double Electron trigger.{\bf FIX! Get the correct numbers in here.}} |
| 451 |
> |
\label{tab:eleTPtrigTrailing} |
| 452 |
|
\end{center} |
| 453 |
|
\end{table} |
| 454 |
|
|
| 455 |
|
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
| 456 |
|
|
| 463 |
– |
|
| 464 |
– |
%figs/mueff/Run2011A_HLT_Mu13_Mu8_trailing/default/extra/dat_eff_table.tex |
| 465 |
– |
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
| 466 |
– |
\begin{table}[!ht] |
| 467 |
– |
\begin{center} |
| 468 |
– |
\begin{tabular}{c|c|c|c|c} |
| 469 |
– |
\hline & $0 < |\eta| < 0.8$ & $0.8 < |\eta| < 1.2$ & $1.2 < |\eta| < 2.1$ & $2.1 < |\eta| < 2.4$ \\ |
| 470 |
– |
\hline |
| 471 |
– |
$ 5 < p_T < 10$ & $0.6916 \pm 0.0337$ & $0.5872 \pm 0.0305$ & $0.5293 \pm 0.0135$ & $0.4288 \pm 0.0217$ \\ |
| 472 |
– |
$ 10 < p_T < 15$ & $0.9685 \pm 0.0053$ & $0.9514 \pm 0.0064$ & $0.9507 \pm 0.0038$ & $0.9048 \pm 0.0090$ \\ |
| 473 |
– |
$ 15 < p_T < 20$ & $0.9700 \pm 0.0025$ & $0.9584 \pm 0.0036$ & $0.9589 \pm 0.0023$ & $0.9169 \pm 0.0058$ \\ |
| 474 |
– |
$ 20 < p_T < 30$ & $0.9671 \pm 0.0009$ & $0.9573 \pm 0.0015$ & $0.9586 \pm 0.0010$ & $0.9154 \pm 0.0026$ \\ |
| 475 |
– |
$ 30 < p_T < 40$ & $0.9691 \pm 0.0005$ & $0.9562 \pm 0.0010$ & $0.9576 \pm 0.0007$ & $0.9129 \pm 0.0020$ \\ |
| 476 |
– |
$ 40 < p_T < 50$ & $0.9691 \pm 0.0005$ & $0.9582 \pm 0.0009$ & $0.9574 \pm 0.0007$ & $0.9129 \pm 0.0021$ \\ |
| 477 |
– |
$ 50 < p_T < 100$ & $0.9694 \pm 0.0009$ & $0.9561 \pm 0.0016$ & $0.9543 \pm 0.0012$ & $0.9058 \pm 0.0039$ \\ |
| 478 |
– |
$100 < p_T < 7000$ & $0.9662 \pm 0.0054$ & $0.9529 \pm 0.0096$ & $0.9443 \pm 0.0083$ & $0.9577 \pm 0.0394$ \\ |
| 479 |
– |
\hline |
| 480 |
– |
\end{tabular} |
| 481 |
– |
\caption{Write some stuff} |
| 482 |
– |
\label{tab:mytable} |
| 483 |
– |
\end{center} |
| 484 |
– |
\end{table} |
| 485 |
– |
|
| 486 |
– |
%KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS |
