Annotation of ttbar/p20_taujets_note/ObjectID_Bkg_and_Dataset.tex, revision 1.1
1.1 ! uid12904 1: \section{Object Identification \label{sec:objects}}
! 2:
! 3: \noindent In this section we describe the main objects used in this study: \met,
! 4: jets and hadronic tau candidates.
! 5:
! 6:
! 7: \subsection{\label{sub:tau--ID}\boldmath Taus}
! 8:
! 9: %\subsubsection{Tau decay modes}
! 10:
! 11: \noindent Taus are reconstructed in the D{\O} detector from energy in the calorimeter and one or more
! 12: tracks.The tau reconstruction algorithm uses a cone of
! 13: $\Delta R = \sqrt{{\delta \eta}^{2} + {\delta \phi}^{2}} < 0.5$ and an inner cone of $\Delta R < 0.3$
! 14: is used to calculate tau isolation variables.
! 15:
! 16: For us, the most important discriminating variables for $\tau$-leptons are \cite{tau-id}:
! 17:
! 18: \begin{itemize}
! 19: \item Profile - $\frac{E_{T}^{1}+E_{T}^{2}}{\sum_{i}E_{T}^{i}}$, where
! 20: $E_{T}^{i}$ is the $E_{T}$ of the $i^{\rm th}$ highest $E_{T}$ tower in
! 21: the cluster.
! 22: \item Isolation, defined as $\frac{E(0.5)-E(0.3)}{E(0.3)}$, where $E(R)$
! 23: is the energy contained in a $y,\phi$ of radius $R$ around the calorimeter cluster centroid.
! 24: \item Track isolation, defined as scalar sum of the $p_{T}'$ of non-$\tau$ tracks in a $\eta,\phi$
! 25: cone of 0.5 around the calorimeter cluster centroid divided by similar sum for tracks associated with $\tau$.
! 26: \end{itemize}
! 27:
! 28: Such variables are chosen based on the possible tau decays:
! 29:
! 30:
! 31: \begin{itemize}
! 32: \item electron or muon ($\tau\rightarrow e\nu_{e}\nu_{\tau}$ or $\tau\rightarrow\mu\nu_{\mu}\nu_{\tau})$,
! 33: BR = 35\% .
! 34: \item single charged hadron and no neutral hadrons ($\tau\rightarrow\pi^{-}\nu_{\tau}$), BR =12\% .
! 35: \item single charged hadron + $\geq1$ neutral
! 36: hadron (i.e., $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow (\pi^0+\pi^{-})\nu_{\tau}$)
! 37: , BR = 38\% .
! 38: \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called
! 39: {}``3-prong'' decays).
! 40: \end{itemize}
! 41:
! 42:
! 43: \noindent which leads us classificate reconstructed taus into three different types
! 44: depending on the number of tracks and electromagnetic (EM) clusters \cite{PDG}:
! 45:
! 46: \begin{enumerate}
! 47: \item {\bf Type1}: calorimeter cluster, one matched charged track and no associated EM subcluster.
! 48: Mainly $\tau\rightarrow\pi^{-}\nu_{\tau}$.
! 49: \item {\bf Type2}: calorimeter cluster, one matched charged track and one or more associated EM subclusters.
! 50: Mainly $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow \pi^0\pi^{-}\nu_{\tau}$.
! 51: \item {\bf Type3}: calorimeter cluster, two or more matched charged tracks and with or without EM subcluster.
! 52: Mainly $\tau\rightarrow\pi^{-}\pi^{-}\pi^{+}(\pi^{0})\nu_{\tau}$.
! 53: \end{enumerate}
! 54:
! 55: In order to provide an optimal tau identification,three Neural Networks (NNs) are trained to
! 56: identify the three types of the taus (1,2 and 3).
! 57:
! 58: The output of these NNs provides a set of three variables ({\tt{nnout}} = 1,2,3)
! 59: to be used to select the tau in the event. The types roughly
! 60: correspond to the $\tau$ lepton decay modes. High values
! 61: of NN correspond to the physical taus, while low ones
! 62: should indicate jets misidentified as taus (fakes).
! 63:
! 64: \subsection{\label{sub:jet--ID}\boldmath Jets}
! 65:
! 66: \noindent Jets are identified using the RunII cone algorithm \cite{jet-id} with cone size of
! 67: $\Delta R < 0.5$. The jet algorithm T42 \cite{t42} is ran before jet reconstruction
! 68: to remove isolated small energy deposits due to noise. D\O\ standard jet quality cuts \cite{jet-qual}
! 69: include L1 Trigger information, calorimeter electromagnetic fraction and coarse hadronic fraction.
! 70:
! 71: Jets used in this analysis are required to have at least two primary vertex tracks associated to them
! 72: (vertex confirmed jets). It implies that although a calorimeter cluster
! 73: is still reconstructed as a jet, it will be discarded if it has less than 2 associated PV tracks.
! 74: In order to correct the energies of reconstructed jets in data and MC back to
! 75: parton-level energies, we apply certified jet energy scale correction (JES)\cite{jes}. Additionally,
! 76: jets containing a muon with $\Delta R(\mu , jet) < 0.5$ from a $b$-quark decay are corrected to take into
! 77: account the momentum carried away by the muon and the neutrino \cite{jesmu}.
! 78:
! 79:
! 80: \subsection{\label{sub:met-id}\boldmath \met}
! 81:
! 82: \noindent Presence of neutrinos in an event causes an imbalance of energy in the transverse plane (\met).
! 83: This quantity is calculated from the transverse energies of all calorimeter cells that pass the
! 84: T42 algorithm, except those of the coarse hadronic layers due to high noise level. However, they are included
! 85: in the case that they are clustered within a reconstructed jet. This raw \met is corrected for the energies
! 86: of other objects like photons, electrons, taus and jets. As muons deposit only a small portion of their
! 87: energy in the calorimeter, their momenta is subtracted from the \met vector.
! 88:
! 89:
! 90: \subsection{The Neural Network b-tagging Algorithm \label{sec:nntag}}
! 91:
! 92: Being QCD and $W$ + jets the main sources of backgrounds in this analysis, requiring
! 93: the presence of at least one jet coming from a $b$-quark is a very powerful method of background
! 94: rejection. The $b$-tagging algorithm used in this measurement is a
! 95: Neural Network (NN) tagging algorithm developed by the b-ID group \cite{bID-p20},
! 96: which combines 7 characteristic variables of SVT, JLIP and CSIP tagging algorithms into the NN
! 97: discriminant. As in the previous analysis we have chosen the operating
! 98: point TIGHT, which is equivalent to requiring the NN discriminant output to be greater than 0.775.
! 99: Both the average efficiency and fake rate are comparable between this p20 version of the
! 100: algorithm and the version used in p17 \cite{bID-p17}.
! 101:
! 102: \paragraph{$b$-tagging efficiency}
! 103:
! 104: In data we apply the b-tagging algorithm directly to jets selected in our sample. In MC such ``direct tag''
! 105: is not done. Instead we have to apply a certain efficiency to MC samples. This inclusive b-decay efficiency ($\epsilon_{b}$)
! 106: is measured in data and it is the product of the probability to tag a b-jet in an MC sample ($\epsilon_{b}^{MC}$)
! 107: containing inclusive decays of the b quark times a scale factor. This data/MC scale factor is given by the ratio
! 108: of data semileptonic efficiency ($\epsilon^{DATA}_{b\rightarrow \mu}$) and a MC semileptonic efficiency ($\epsilon^{MC}_{b\rightarrow \mu}$).
! 109: This scale factor, that measures the effect on the tagging rate
! 110: caused by the differences in tracking between data and MC, is then used to properly scale
! 111: the MC-derived efficiency. It is assumed that such factor could be applied to any MC tagging efficiency \cite{bID-p20}.
! 112:
! 113: \paragraph{$c$-tagging efficiency}
! 114:
! 115: It is assumed that the same procedure adopted in the b-jet case is also valid for c-jets,
! 116: namely, a c-jet scale factor ($\epsilon^{DATA}_{b\rightarrow \mu}$)/($\epsilon^{MC}_{b\rightarrow \mu}$)
! 117: multiplies the probability to tag a c-jet in an MC sample ($\epsilon_{b}^{MC}$) to get the
! 118: c-tagging efficiency.
! 119:
! 120:
! 121: \paragraph{Light jet tagging efficiency}
! 122:
! 123: The $b$-tag fake rate from light quarks is computed by measuring the
! 124: negative tag rate as defined in \cite{b_fake}. The method uses fits of
! 125: b-, c- and light jets tagging rates to binned data combined with sample
! 126: composition estimated from data. Binned NN output ($NN_{out}$) distributions
! 127: for b, c and light jets is fitted to data distribution using b- and c-jet efficiencies
! 128: provided by standard bID TRF's.
! 129:
! 130:
! 131:
! 132: \paragraph{Taggability}
! 133:
! 134: $b$-tag algorithm can't be applied to any jet, but only the ones that contain tracks.
! 135: Such jets are called ``taggable'' and are defined by matching within $\Delta R$ $<$ 0.5 to
! 136: a track jet, composed of at least two tracks.
! 137: Just like b-tagging, taggability is different in data and MC, due to imperfect
! 138: simulation of tracking system. To account for this, taggability rate functions are applied
! 139: to MC events. Such functions are parametrized in terms of jet $p_{T}$, jet $\eta$ and
! 140: primary vertex $z$. They were derived on the single top loose data samples, where the isolation
! 141: quality of the lepton is loose.The validity of these functions is tested by comparing observed
! 142: and predicted taggability to tight samples. The results show a good agreement as described in \cite{single top}.
! 143:
! 144: \clearpage
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