--- ttbar/p20_taujets_note/ObjectID_Bkg_and_Dataset.tex 2011/05/18 21:30:39 1.1 +++ ttbar/p20_taujets_note/ObjectID_Bkg_and_Dataset.tex 2011/06/01 01:20:54 1.2 @@ -1,16 +1,16 @@ \section{Object Identification \label{sec:objects}} \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} %\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 -$\Delta R = \sqrt{{\delta \eta}^{2} + {\delta \phi}^{2}} < 0.5$ 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. For us, the most important discriminating variables for $\tau$-leptons are \cite{tau-id}: @@ -20,8 +20,8 @@ For us, the most important discriminatin $E_{T}^{i}$ is the $E_{T}$ of the $i^{\rm th}$ highest $E_{T}$ tower in the cluster. \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. -\item Track isolation, defined as scalar sum of the $p_{T}'$ of non-$\tau$ tracks in a $\eta,\phi$ +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 $p_{T}$'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 $\tau$. \end{itemize} @@ -33,27 +33,26 @@ Such variables are chosen based on the p BR = 35\% . \item single charged hadron and no neutral hadrons ($\tau\rightarrow\pi^{-}\nu_{\tau}$), BR =12\% . \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\% . \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called {}``3-prong'' decays). \end{itemize} -\noindent which leads us classificate reconstructed taus into three different types -depending on the number of tracks and electromagnetic (EM) clusters \cite{PDG}: +\noindent Reconstruction of hadronic decay of taus results in classification of a tau candidate +in one of the following three types \cite{PDG}: \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 $\tau\rightarrow\pi^{-}\nu_{\tau}$. -\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 $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow \pi^0\pi^{-}\nu_{\tau}$. -\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 $\tau\rightarrow\pi^{-}\pi^{-}\pi^{+}(\pi^{0})\nu_{\tau}$. \end{enumerate} -In order to provide an optimal tau identification,three Neural Networks (NNs) are trained to -identify the three types of the taus (1,2 and 3). +A seperate NN is trained to identify each type of tau. 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 @@ -63,33 +62,35 @@ should indicate jets misidentified as ta \subsection{\label{sub:jet--ID}\boldmath Jets} -\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 -to remove isolated small energy deposits due to noise. D\O\ standard jet quality cuts \cite{jet-qual} +\noindent Jets are identified using the Run II cone algorithm \cite{jet-id} with cone size of +$\Delta R < 0.5$. The jet algorithm T42 \cite{t42} is run before jet reconstruction +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. 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. 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, -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}. \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 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 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. +\subsection{b jets \label{sec:nntag}} -\subsection{The Neural Network b-tagging Algorithm \label{sec:nntag}} - -Being QCD and $W$ + jets the main sources of backgrounds in this analysis, requiring +Since the main sources of background in this analysis are QCD and $W$ + jets, requiring 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 Neural Network (NN) tagging algorithm developed by the b-ID group \cite{bID-p20}, @@ -104,9 +105,9 @@ 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'' 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 ($\epsilon_{b}^{MC}$) -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 ($\epsilon^{MC}_{b\rightarrow \mu}$). -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 the MC-derived efficiency. It is assumed that such factor could be applied to any MC tagging efficiency \cite{bID-p20}.