Diff for /ttbar/p20_taujets_note/ObjectID_Bkg_and_Dataset.tex between versions 1.1 and 1.2

version 1.1, 2011/05/18 21:30:39 version 1.2, 2011/06/01 01:20:54
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 \section{Object Identification \label{sec:objects}}  \section{Object Identification \label{sec:objects}}
   
 \noindent In this section we describe the main objects used in this study: \met,   \noindent In this section we describe the main objects used in this study: \met, 
 jets and hadronic tau candidates.  jets, $b$ jets and hadronic tau candidates.
   
   
 \subsection{\label{sub:tau--ID}\boldmath Taus}  \subsection{\label{sub:tau--ID}\boldmath Taus}
   
 %\subsubsection{Tau decay modes}  %\subsubsection{Tau decay modes}
   
 \noindent Taus are reconstructed in the D{\O} detector from energy in the calorimeter and one or more   \noindent Taus are reconstructed in the D0 detector from energy in the calorimeter and one or more 
 tracks.The tau reconstruction algorithm uses a cone of   tracks.The tau reconstruction algorithm uses a cone of 
 $\Delta R = \sqrt{{\delta \eta}^{2} + {\delta \phi}^{2}} < 0.5$ and an inner cone of $\Delta R < 0.3$  $\Delta R = \sqrt{{(\Delta \eta})^{2} + {(\Delta \phi})^{2}} < 0.5$ and an inner cone of $\Delta R < 0.3$
 is used to calculate tau isolation variables.   is used to calculate tau isolation variables. 
   
 For us, the most important discriminating variables for $\tau$-leptons are \cite{tau-id}:  For us, the most important discriminating variables for $\tau$-leptons are \cite{tau-id}:
Line 20  For us, the most important discriminatin Line 20  For us, the most important discriminatin
 $E_{T}^{i}$ is the $E_{T}$ of the $i^{\rm th}$ highest $E_{T}$ tower in  $E_{T}^{i}$ is the $E_{T}$ of the $i^{\rm th}$ highest $E_{T}$ tower in
 the cluster.  the cluster.
 \item Isolation, defined as $\frac{E(0.5)-E(0.3)}{E(0.3)}$, where $E(R)$  \item Isolation, defined as $\frac{E(0.5)-E(0.3)}{E(0.3)}$, where $E(R)$
 is the energy contained in a $y,\phi$ of radius $R$ around the calorimeter cluster centroid.  is the energy contained in a $\eta,\phi$ of radius $R$ around the calorimeter cluster centroid.
 \item Track isolation, defined as scalar sum of the $p_{T}'$ of non-$\tau$ tracks in a $\eta,\phi$  \item Track isolation, defined as scalar sum of the $p_{T}$'s of non-$\tau$ tracks in a $\eta,\phi$
 cone of 0.5 around the calorimeter cluster centroid divided by similar sum for tracks associated with $\tau$.  cone of 0.5 around the calorimeter cluster centroid divided by similar sum for tracks associated with $\tau$.
 \end{itemize}  \end{itemize}
   
Line 33  Such variables are chosen based on the p Line 33  Such variables are chosen based on the p
 BR = 35\% .  BR = 35\% .
 \item single charged hadron and no neutral hadrons ($\tau\rightarrow\pi^{-}\nu_{\tau}$), BR =12\% .  \item single charged hadron and no neutral hadrons ($\tau\rightarrow\pi^{-}\nu_{\tau}$), BR =12\% .
 \item single charged hadron + $\geq1$ neutral   \item single charged hadron + $\geq1$ neutral 
 hadron (i.e.,  $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow (\pi^0+\pi^{-})\nu_{\tau}$)  hadron (i.e.,  $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow (\pi^0\pi^{-})\nu_{\tau}$)
 , BR = 38\% .  , BR = 38\% .
 \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called  \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called
 {}``3-prong'' decays).   {}``3-prong'' decays). 
 \end{itemize}  \end{itemize}
   
   
 \noindent which leads us classificate reconstructed taus into three different types  \noindent Reconstruction of hadronic decay of taus  results in classification of a tau candidate
 depending on the number of tracks and electromagnetic (EM) clusters \cite{PDG}:  in one of the following three types \cite{PDG}:
   
 \begin{enumerate}  \begin{enumerate}
 \item {\bf Type1}: calorimeter cluster, one matched charged track and no associated EM subcluster.   \item {\bf Type1}: calorimeter cluster, one matched track and no associated EM subcluster. 
 Mainly $\tau\rightarrow\pi^{-}\nu_{\tau}$.  Mainly $\tau\rightarrow\pi^{-}\nu_{\tau}$.
 \item {\bf Type2}: calorimeter cluster, one matched charged track and one or more associated EM subclusters.   \item {\bf Type2}: calorimeter cluster, one matched  track and one or more associated EM subclusters. 
 Mainly $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow \pi^0\pi^{-}\nu_{\tau}$.  Mainly $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow \pi^0\pi^{-}\nu_{\tau}$.
 \item {\bf Type3}: calorimeter cluster, two or more matched charged tracks and with or without EM subcluster.   \item {\bf Type3}: calorimeter cluster, two or more matched tracks and with or without EM subcluster. 
 Mainly $\tau\rightarrow\pi^{-}\pi^{-}\pi^{+}(\pi^{0})\nu_{\tau}$.  Mainly $\tau\rightarrow\pi^{-}\pi^{-}\pi^{+}(\pi^{0})\nu_{\tau}$.
 \end{enumerate}  \end{enumerate}
   
 In order to provide an optimal tau identification,three Neural Networks (NNs) are trained to  A seperate NN is trained to identify each type of tau.
 identify the three types of the taus (1,2 and 3).  
   
 The output of these NNs provides a set of three variables ({\tt{nnout}} = 1,2,3)  The output of these NNs provides a set of three variables ({\tt{nnout}} = 1,2,3)
 to be used to select the tau in the event. The types roughly   to be used to select the tau in the event. The types roughly 
Line 64  should indicate jets misidentified as ta Line 63  should indicate jets misidentified as ta
 \subsection{\label{sub:jet--ID}\boldmath Jets}  \subsection{\label{sub:jet--ID}\boldmath Jets}
   
 \noindent Jets are identified using the RunII cone algorithm \cite{jet-id} with cone size of  \noindent Jets are identified using the RunII cone algorithm \cite{jet-id} with cone size of
 $\Delta R < 0.5$. The jet algorithm T42 \cite{t42} is ran before jet reconstruction  $\Delta R < 0.5$. The jet algorithm T42 \cite{t42} is run before jet reconstruction
 to remove isolated small energy deposits due to noise. D\O\ standard jet quality cuts \cite{jet-qual}  to remove isolated small energy deposits due to noise. D0 standard jet quality cuts \cite{jet-qual}
 include L1 Trigger information, calorimeter electromagnetic fraction and coarse hadronic fraction.  include L1 Trigger information, calorimeter electromagnetic fraction and coarse hadronic fraction.
   
 Jets used in this analysis are required to have at least two primary vertex tracks associated to them  Jets used in this analysis are required to have at least two primary vertex tracks associated to them
 (vertex confirmed jets). It implies that although a calorimeter cluster  (vertex confirmed jets). This choice was motivated by a better agreement between data and
   MC, a better modeling in the ICD region of the calorimeter and the fact that all b-ID
   studies were done using this kind of jets. It implies that although a calorimeter cluster
 is still reconstructed as a jet, it will be discarded if it has less than 2 associated PV tracks.  is still reconstructed as a jet, it will be discarded if it has less than 2 associated PV tracks.
 In order to correct the energies of reconstructed jets in data and MC back to  In order to correct the energies of reconstructed jets in data and MC back to
 parton-level energies, we apply certified jet energy scale correction (JES)\cite{jes}. Additionally,  parton-level energies, we apply certified jet energy scale correction (JES)\cite{jes}. Additionally,
 jets containing a muon with $\Delta R(\mu , jet) < 0.5$ from a $b$-quark decay are corrected to take into  jets containing a muon with $\Delta R(\mu , jet) = 0.5$ from heavy quark decays are corrected to take into
 account the momentum carried away by the muon and the neutrino \cite{jesmu}.  account the momentum carried away by the muon and the neutrino \cite{jesmu}.
   
   
 \subsection{\label{sub:met-id}\boldmath \met}  \subsection{\label{sub:met-id}\boldmath \met}
   
 \noindent Presence of neutrinos in an event causes an imbalance of energy in the transverse plane (\met).  \noindent Presence of neutrinos in an event is inferred from an imbalance of the component 
   net momentum in the plane perpendicular  to the beam (transverse plane).
 This quantity is calculated from the transverse energies of all calorimeter cells that pass the   This quantity is calculated from the transverse energies of all calorimeter cells that pass the 
 T42 algorithm, except those of the coarse hadronic layers due to high noise level. However, they are included  T42 algorithm, except those of the coarse hadronic layers due to high noise level. However, they are included
 in the case that they are clustered within a reconstructed jet. This raw \met is corrected for the energies  in the case that they are clustered within a reconstructed jet. This raw \met is corrected for the energies
 of other objects like photons, electrons, taus and jets. As muons deposit only a small portion of their  of other objects like photons, electrons, taus and jets. As muons deposit only a small portion of their
 energy in the calorimeter, their momenta is subtracted from the \met vector.  energy in the calorimeter, their momenta is subtracted from the \met vector.
   
   \subsection{b jets \label{sec:nntag}}
   
 \subsection{The Neural Network b-tagging Algorithm \label{sec:nntag}}  Since the main sources of background in this analysis are QCD and $W$ + jets, requiring
   
 Being QCD and $W$ + jets the main sources of backgrounds in this analysis, requiring  
 the presence of at least one jet coming from a $b$-quark is a very powerful method of background   the presence of at least one jet coming from a $b$-quark is a very powerful method of background 
 rejection. The $b$-tagging algorithm used in this measurement is a  rejection. The $b$-tagging algorithm used in this measurement is a
 Neural Network (NN) tagging algorithm developed by the b-ID group \cite{bID-p20},  Neural Network (NN) tagging algorithm developed by the b-ID group \cite{bID-p20},
Line 104  algorithm and the version used in p17 \c Line 105  algorithm and the version used in p17 \c
 In data we apply the b-tagging algorithm directly to jets selected in our sample. In MC such ``direct tag''  In data we apply the b-tagging algorithm directly to jets selected in our sample. In MC such ``direct tag''
 is not done. Instead we have to apply a certain efficiency to MC samples. This inclusive b-decay efficiency ($\epsilon_{b}$)  is not done. Instead we have to apply a certain efficiency to MC samples. This inclusive b-decay efficiency ($\epsilon_{b}$)
 is measured in data and it is the product of the probability to tag a b-jet in an MC sample ($\epsilon_{b}^{MC}$)  is measured in data and it is the product of the probability to tag a b-jet in an MC sample ($\epsilon_{b}^{MC}$)
 containing inclusive decays of the b quark times a scale factor. This data/MC scale factor is given by the ratio  containing inclusive decays of the b quark and a scale factor. This data/MC scale factor is given by the ratio
 of data semileptonic efficiency ($\epsilon^{DATA}_{b\rightarrow \mu}$) and a MC semileptonic efficiency ($\epsilon^{MC}_{b\rightarrow \mu}$).  of data semileptonic efficiency ($\epsilon^{DATA}_{b\rightarrow \mu}$) and a MC semileptonic efficiency ($\epsilon^{MC}_{b\rightarrow \mu}$).
 This scale factor, that measures the effect on the tagging rate  This scale factor, which measures the effect on the tagging rate
 caused by the differences in tracking between data and MC, is then used to properly scale  caused by the differences in tracking between data and MC, is then used to properly scale
 the MC-derived efficiency. It is assumed that such factor could be applied to any MC tagging efficiency \cite{bID-p20}.  the MC-derived efficiency. It is assumed that such factor could be applied to any MC tagging efficiency \cite{bID-p20}.
   

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