Annotation of ttbar/p20_taujets_note/ObjectID_Bkg_and_Dataset.tex, revision 1.2
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,
1.2 ! uid12904 4: jets, $b$ jets and hadronic tau candidates.
1.1 uid12904 5:
6:
7: \subsection{\label{sub:tau--ID}\boldmath Taus}
8:
9: %\subsubsection{Tau decay modes}
10:
1.2 ! uid12904 11: \noindent Taus are reconstructed in the D0 detector from energy in the calorimeter and one or more
1.1 uid12904 12: tracks.The tau reconstruction algorithm uses a cone of
1.2 ! uid12904 13: $\Delta R = \sqrt{{(\Delta \eta})^{2} + {(\Delta \phi})^{2}} < 0.5$ and an inner cone of $\Delta R < 0.3$
1.1 uid12904 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 $E(R)$
1.2 ! uid12904 23: is the energy contained in a $\eta,\phi$ of radius $R$ around the calorimeter cluster centroid.
! 24: \item Track isolation, defined as scalar sum of the $p_{T}$'s of non-$\tau$ tracks in a $\eta,\phi$
1.1 uid12904 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
1.2 ! uid12904 36: hadron (i.e., $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow (\pi^0\pi^{-})\nu_{\tau}$)
1.1 uid12904 37: , BR = 38\% .
38: \item 3 charged hadrons + $\geq0$ neutral hadrons, BR = 15\% (so-called
39: {}``3-prong'' decays).
40: \end{itemize}
41:
42:
1.2 ! uid12904 43: \noindent Reconstruction of hadronic decay of taus results in classification of a tau candidate
! 44: in one of the following three types \cite{PDG}:
1.1 uid12904 45:
46: \begin{enumerate}
1.2 ! uid12904 47: \item {\bf Type1}: calorimeter cluster, one matched track and no associated EM subcluster.
1.1 uid12904 48: Mainly $\tau\rightarrow\pi^{-}\nu_{\tau}$.
1.2 ! uid12904 49: \item {\bf Type2}: calorimeter cluster, one matched track and one or more associated EM subclusters.
1.1 uid12904 50: Mainly $\tau\rightarrow\rho^{-}\nu_{\tau}\rightarrow \pi^0\pi^{-}\nu_{\tau}$.
1.2 ! uid12904 51: \item {\bf Type3}: calorimeter cluster, two or more matched tracks and with or without EM subcluster.
1.1 uid12904 52: Mainly $\tau\rightarrow\pi^{-}\pi^{-}\pi^{+}(\pi^{0})\nu_{\tau}$.
53: \end{enumerate}
54:
1.2 ! uid12904 55: A seperate NN is trained to identify each type of tau.
1.1 uid12904 56:
57: The output of these NNs provides a set of three variables ({\tt{nnout}} = 1,2,3)
58: to be used to select the tau in the event. The types roughly
59: correspond to the $\tau$ lepton decay modes. High values
60: of NN correspond to the physical taus, while low ones
61: should indicate jets misidentified as taus (fakes).
62:
63: \subsection{\label{sub:jet--ID}\boldmath Jets}
64:
1.2 ! uid12904 65: \noindent Jets are identified using the Run II cone algorithm \cite{jet-id} with cone size of
! 66: $\Delta R < 0.5$. The jet algorithm T42 \cite{t42} is run before jet reconstruction
! 67: to remove isolated small energy deposits due to noise. D0 standard jet quality cuts \cite{jet-qual}
1.1 uid12904 68: include L1 Trigger information, calorimeter electromagnetic fraction and coarse hadronic fraction.
69:
70: Jets used in this analysis are required to have at least two primary vertex tracks associated to them
1.2 ! uid12904 71: (vertex confirmed jets). This choice was motivated by a better agreement between data and
! 72: MC, a better modeling in the ICD region of the calorimeter and the fact that all b-ID
! 73: studies were done using this kind of jets. It implies that although a calorimeter cluster
1.1 uid12904 74: is still reconstructed as a jet, it will be discarded if it has less than 2 associated PV tracks.
75: In order to correct the energies of reconstructed jets in data and MC back to
76: parton-level energies, we apply certified jet energy scale correction (JES)\cite{jes}. Additionally,
1.2 ! uid12904 77: jets containing a muon with $\Delta R(\mu , jet) = 0.5$ from heavy quark decays are corrected to take into
1.1 uid12904 78: account the momentum carried away by the muon and the neutrino \cite{jesmu}.
79:
80:
81: \subsection{\label{sub:met-id}\boldmath \met}
82:
1.2 ! uid12904 83: \noindent Presence of neutrinos in an event is inferred from an imbalance of the component
! 84: net momentum in the plane perpendicular to the beam (transverse plane).
1.1 uid12904 85: This quantity is calculated from the transverse energies of all calorimeter cells that pass the
86: T42 algorithm, except those of the coarse hadronic layers due to high noise level. However, they are included
87: in the case that they are clustered within a reconstructed jet. This raw \met is corrected for the energies
88: of other objects like photons, electrons, taus and jets. As muons deposit only a small portion of their
89: energy in the calorimeter, their momenta is subtracted from the \met vector.
90:
1.2 ! uid12904 91: \subsection{b jets \label{sec:nntag}}
1.1 uid12904 92:
1.2 ! uid12904 93: Since the main sources of background in this analysis are QCD and $W$ + jets, requiring
1.1 uid12904 94: the presence of at least one jet coming from a $b$-quark is a very powerful method of background
95: rejection. The $b$-tagging algorithm used in this measurement is a
96: Neural Network (NN) tagging algorithm developed by the b-ID group \cite{bID-p20},
97: which combines 7 characteristic variables of SVT, JLIP and CSIP tagging algorithms into the NN
98: discriminant. As in the previous analysis we have chosen the operating
99: point TIGHT, which is equivalent to requiring the NN discriminant output to be greater than 0.775.
100: Both the average efficiency and fake rate are comparable between this p20 version of the
101: algorithm and the version used in p17 \cite{bID-p17}.
102:
103: \paragraph{$b$-tagging efficiency}
104:
105: In data we apply the b-tagging algorithm directly to jets selected in our sample. In MC such ``direct tag''
106: is not done. Instead we have to apply a certain efficiency to MC samples. This inclusive b-decay efficiency ($\epsilon_{b}$)
107: is measured in data and it is the product of the probability to tag a b-jet in an MC sample ($\epsilon_{b}^{MC}$)
1.2 ! uid12904 108: containing inclusive decays of the b quark and a scale factor. This data/MC scale factor is given by the ratio
1.1 uid12904 109: of data semileptonic efficiency ($\epsilon^{DATA}_{b\rightarrow \mu}$) and a MC semileptonic efficiency ($\epsilon^{MC}_{b\rightarrow \mu}$).
1.2 ! uid12904 110: This scale factor, which measures the effect on the tagging rate
1.1 uid12904 111: caused by the differences in tracking between data and MC, is then used to properly scale
112: the MC-derived efficiency. It is assumed that such factor could be applied to any MC tagging efficiency \cite{bID-p20}.
113:
114: \paragraph{$c$-tagging efficiency}
115:
116: It is assumed that the same procedure adopted in the b-jet case is also valid for c-jets,
117: namely, a c-jet scale factor ($\epsilon^{DATA}_{b\rightarrow \mu}$)/($\epsilon^{MC}_{b\rightarrow \mu}$)
118: multiplies the probability to tag a c-jet in an MC sample ($\epsilon_{b}^{MC}$) to get the
119: c-tagging efficiency.
120:
121:
122: \paragraph{Light jet tagging efficiency}
123:
124: The $b$-tag fake rate from light quarks is computed by measuring the
125: negative tag rate as defined in \cite{b_fake}. The method uses fits of
126: b-, c- and light jets tagging rates to binned data combined with sample
127: composition estimated from data. Binned NN output ($NN_{out}$) distributions
128: for b, c and light jets is fitted to data distribution using b- and c-jet efficiencies
129: provided by standard bID TRF's.
130:
131:
132:
133: \paragraph{Taggability}
134:
135: $b$-tag algorithm can't be applied to any jet, but only the ones that contain tracks.
136: Such jets are called ``taggable'' and are defined by matching within $\Delta R$ $<$ 0.5 to
137: a track jet, composed of at least two tracks.
138: Just like b-tagging, taggability is different in data and MC, due to imperfect
139: simulation of tracking system. To account for this, taggability rate functions are applied
140: to MC events. Such functions are parametrized in terms of jet $p_{T}$, jet $\eta$ and
141: primary vertex $z$. They were derived on the single top loose data samples, where the isolation
142: quality of the lepton is loose.The validity of these functions is tested by comparing observed
143: and predicted taggability to tight samples. The results show a good agreement as described in \cite{single top}.
144:
145: \clearpage
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