version 1.1, 2011/05/18 21:30:39
|
version 1.2, 2011/06/01 01:20:54
|
Line 1
|
Line 1
|
\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}. |
|
|