| 9 |
|
%__________________________________________________ |
| 10 |
|
\subsubsection{Offline Muon Selection}\label{sec:muOffline} |
| 11 |
|
%__________________________________________________ |
| 12 |
< |
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 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. |
| 12 |
> |
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. |
| 13 |
|
|
| 14 |
|
%------------------------------------------------- |
| 15 |
|
\begin{table}[tbh] |
| 68 |
|
%------------------------------------------------- |
| 69 |
|
|
| 70 |
|
% |
| 71 |
< |
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 $p_{T}$ region. We require events containing at least one muon candidate that passes 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. |
| 71 |
> |
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. |
| 72 |
|
|
| 73 |
|
%------------------------------------------------- |
| 74 |
|
\begin{figure}[htb] |
