COMP/CSA06DOC/analysisdemo.tex

\section {Analysis Demonstrations}

A wide variety of physics analysis demonstrations were
prepared by the 
physics groups for CSA06 in order to test the analysis workflow for
CSA06. The number of analyses is approximately 28, with nearly 70
active participants.
These demonstrations proved to be useful training
exercises for collaborators in the new software and computing tools as well.
The list of specialized calibration and
alignment streams produced at the Tier-0 and analyzed is discussed in
Section~\ref{sec:offlineswalca}. The list of general physics analysis
skims produced at the Tier-1 centres is covered in
Table~\ref{tab:tier1skim}. Table~\ref{tab:analyses} lists the physics
analyses that were conducted as part of CSA06. Details on these
analyses are contained in the following subsections. 
%Unless noted,
%CRAB was used to submit the analysis jobs.


\begin{table}[phtb]
\centering
\caption{List of CSA06 analyses.}
\vspace{3mm}
\label{tab:analyses}
\begin{tabular}{|l|l|l|l|}
\hline
Group &  Analysis & People & Dataset   \\ \hline
eg & ECAL isol. electron calib.  & L.Agostino (CERN), P. Govoni (Milan),  & AlCaReco \\
   & & L.Malgeri (CERN), R.Ofierzynski (CERN) & \\
eg & ECAL Phi symmetry calib. & D.Futyan (IC) & minbias AlCaReco \\
eg & Z$\rightarrow ee$ reco.  & P.Meridiani  (Rome) & Zee AlCaReco  \\
\hline
jm & HCAL Phi symmetry calib. & O.Kodolova (Moscow) & minbias AlCaReco \\
jm & HCAL isol. trk. calib. & M.Szleper (Northwestern),  & minbias AlCaReco \\
 &  &  S.Petrushanko (Moscow) &  \\
jm & Jet calib. & R.Harris, M.Cardaci (FNAL) & Jets \\
\hline
tk & Partial Tracker alignment  & L.Edera, F.Ronga, and
O.Buchmuller (CERN),   & Zmumu AlcaReco  \\
 &   &  F.-P. Schilling (Karlsruhe),   &   \\
tk & Misalignments effects on track reco  & N. De Filippis (Bari)  &   \\
\hline
mu & Muon alignment  & J.Fernandez, P.Martinez, 
  & Muon AlCaReco  \\
 &  &  F.Matorras (Santander)  &  \\
mu & W$\rightarrow\mu\nu$  & M.Biasotto, U.Gasparini, M.Margoni,  & EWK, Soft Muon skims  \\
 & & E.Torassa (Padova)  &   \\
mu & J/Psi and Z$\rightarrow\mu\mu$  &  J.Alcaraz, J.Hernandez,
J.Caballero & EWK, Soft Muon skims  \\
 &   &   P.Garcia Abia (CIEMAT) &   \\
mu & Z$\rightarrow\mu\mu$ and muon effic.  & C.Liu and N.Neumeister (Purdue)  & EWK  skim \\
 & &  M.Schmitt (Northwestern)  &  \\
\hline
hg & Selection of Z$\rightarrow 2\tau$ & 
  K.Petridis (IC), A.Kalinowski (Warsaw) & EWK skims \\
hg & jet$\rightarrow \tau$ mis id & 
  F.Blekman (IC), C.Siamitros (Brunel) & Z+jet\\
hg & tau tagging efficiency from Z$\rightarrow 2\tau$ & 
  S.Gennai and G.Bagliesi (Pisa), & EWK skims \\
 & &  A.Goussiou and R.Vasques Sierra (FNAL) &  \\
hg & tau HLT with pixel trigger using Z$\rightarrow 2\tau$ & 
  D.Kotlinski and P.Trueb (PSI) & EWK skims  \\
hg & Background to H$\rightarrow WW\rightarrow \ell\ell$ & 
  F. Stoeckli (ETH) & TTbar \\ 
hg & Z+2Jet background to qqH, H$\rightarrow$inv & 
  J.Brooke and S.Metson (Bristol), & EWK  skims \\  
   &  & K. Mazumdar, S.Bansal (TIFR) &  \\
\hline
sm & underlying event/minbias & L.Fano and F.Ambroglini (Pisa) &
m.b., Jets, D-Y skims \\
   & & P.Bartalini and R.Field (Florida) & \\
   & & F.Bechtel (Hamburg) & \\
sm & T-Tbar dilepton selection & I.Gonzalez-Caballero (Santander),  & TTbar skim \\
 &  & J.Cuevas Maestro (Oviedo) &  \\
sm & T-Tbar inclusive & I.Gonzalez-Caballero (Santander), & TTbar skims \\
 &  & J.Vizan (Oviedo) & \\
 &  & J.Heynick (Brussels) & \\
 &  & J.Vizan (Oviedo) & \\
sm & W mass & M.Malberti (Milan) & EWK skim \\
\hline
su & LM1 Jets + MET & M.Tytgat, M.Spiropulu (CERN) & Exotic, TTbar skims \\
su & Di-tau + MET & D.J.Mangeol (Strasbourg) & Exotic \\
su & LM1 b-tagging & R.Stringer (Riverside) & Exotic \\
su & Dijet mass & R.Harris, M.Cardaci (FNAL) & QCD, Exotic skims\\
su & High energy $e^+e^-$ & P.Vanlaer (Brussels),
D.Evans (Bristol),  & Exotic, EWK skims \\
   & & C.Shepherd-Themistocleous (RAL) & \\
su & Z$'\rightarrow \mu^+\mu^-$  & A.Lanyov, S.Shmatov (Dubna)  & Exotic \\
\hline
\end{tabular}
\end{table}


\subsection {Calibration}

\label{sec:calib}

\input{calib_ana.tex}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsection {Alignment}

\label{sec:align}

\subsubsection{Tracker Alignment}

A tracker alignment CSA06 exercise was carried out with the goal to
demonstrate the full work- and dataflow of the alignment process.
The exercise followed closely the ideas and concepts
developed during the T0-RTAG~\cite{tier0rtag}.
The exercise comprised the following steps:
\begin{itemize}
\item Reading of alignment constants from the offline database during prompt
reconstruction;
\item Writing dedicated AlCaReco streams for alignment;
\item Defining a misalignment scenario and insertion of the corresponding
object into the offline database;
\item Running an alignment algorithm at the Tier-0;
\item Inserting the resulting alignment corrections into the database;
\item Running re-reconstruction at a Tier-1 centre reading this alignment
object;
\item Running analysis jobs in which ideal, misaligned
and aligned distributions are compared.
\end{itemize}

The steps regarding production of the dedicated AlCaReco streams
as well as enabling the prompt reconstruction to read alignment
constants from the offline database were already described in
section~\ref{sec:offlineswalca}. In the following, a short
summary of the exercise is given. For more details, 
see~\cite{alcacsa06note}.

The following misalignment scenario was defined for this exercise: The
sensors of the Tracker Inner Barrel TIB as well as the Rods of the
Tracker Outer Barrel TOB were misaligned.  Random shifts drawing from
a flat distribution in the range $\pm 100 \rm\ \mu m$ were applied in
$u$, $v$, $w$ (local coordinate system) for layers built from double
sided modules, and in $u$ (precise coordinate) only for layers built
from single sided modules.  In addition, random rotations around all
three local coordinate axes of size $\pm 10 \rm\ mrad$ were applied to
the modules/rods of both single and double sided TIB and TOB layers. 
The pixel detector was kept fixed
in order to define a reference system. In addition, the outermost TOB
layer was also kept fixed in order to improve the convergence. This
misalignment scenario was inserted as an alignment object into the
offline database. In addition, another object corresponding to the
ideal tracker geometry was inserted, to be used during prompt
reconstruction.

\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{figs/csa06_convergence}
\caption{Alignment exercise: The convergence of the alignment
algorithm in $\Delta u, \Delta v, \Delta w$ is shown for double sided
TIB modules as a function of the iteration number (left), as well as
projected for initial misalignment (labeled ``after 0 Iterations'')
and after 4, 7 and 10 iterations (right).  }
\label{fig:alignment_convergence}
\end{figure}

The alignment was performed running the HIP alignment
algorithm~\cite{hipnote} as implemented in CMSSW\_1\_0\_6 over approx. 
$1 \rm\ M$ AlCaReco $Z^0\rightarrow\mu^+\mu^-$ events produced during
prompt reconstruction at the Tier-0, reading the above mentioned
misalignment object from the offline database. The algorithm was run
on 20 dedicated CPUs in parallel at CERN, iterating 10 times over the
data sample. The result of the alignment was obtained in less than $5
\rm\ h$, and the corresponding tracker alignment object
was inserted into the database. Figure~\ref{fig:alignment_convergence}
illustrates the convergence of the alignment for the double sided TIB
sensors.

\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{figs/csa06_aliexercise}
\caption{Invariant mass distribution from $Z^0\to \mu^+ \mu^-$ events,
obtained from events produced by the prompt reconstruction at
the Tier-0 (``ideal''), from events processed with misalignment
as used as input for the alignment algorithm (``misaligned'') and
from events re-reconstructed at a Tier-1 centre (PIC) using the alignment
constants derived from the alignment algorithm (``realigned'').
}
\label{fig:aliexercise}
\end{figure}

Once the alignment and calibration constants were inserted in the
database, they were deployed to the Tier-1/2 centres via Frontier.
Subsequently, re-reconstruction of some of the CSA06 datasets was
launched at various Tier-1 centres. For instance, the
$Z^0\rightarrow\mu^+\mu^-$ data set was re-reconstructed at PIC
(Barcelona) using the new alignment object. In order to demonstrate
the final missing piece of the workflow, grid analysis jobs were
submitted to PIC to process these re-reconstructed
$Z^0\rightarrow\mu^+\mu^-$ events. The reconstructed invariant dimuon
mass is presented in Figure~\ref{fig:aliexercise} for three cases:
\begin{itemize}
\item Using the ideal geometry, reading the RECO produced during prompt
reconstruction;
\item Using the misaligned geometry, reading the AlCaReco and the misalignment
scenario database object;
\item Using the realigned geometry, reading the events re-reconstructed
with the alignment database object.
\end{itemize}
As can be seen, the invariant mass resolution is degraded in the case
of misalignment. After the alignment algorithm has corrected the
tracker geometry, the resolution is recovered close to the original
value. This demonstrates that the work- and dataflow of the full
alignment was successfully carried out.


\input{muonalignment.tex}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsection {Physics Analysis Exercises}

\subsubsection {Effect of tracker misalignment on track reconstruction performances}

The alignment uncertainties of the CMS Tracker detector, made of a huge amount of independent silicon sensors with an excellent position resolution, affect the performances of the track reconstruction and track parameters measurement. 
The analysis exercise performed by the team at Bari during the CSA06 had the purpose of study the effect of the CMS tracker misalignment on the performances of the track reconstruction \cite{misalignment}. Realistic estimates for the expected displacements of the tracking systems were supplied in different scenarios as specified in the following: 
 
\begin{itemize}
\item the ideal scenario with a perfect tracker geometry;
\item the short term misalignment scenario supposed to reproduce the mis-alignment conditions during the first 
data taking when the uncertainties on the position of the sub-structures of the CMS
tracker will be between $10 \, \mu$ for pixel detectors and $400 \, \mu$ for microstrip silicon detectors in 
the endcaps. Detector position and errors are read
 from the offline database at CERN by caching the needed information locally via frontier/squid
software.

\item the long term scenario when the alignment uncertainties are supposed to be a factor 10 smaller because of the
improvement obtained by using alignment algorithms with a high statistics of tracks.
 
\item the CSA06 aligned scenario by using the tracker module position and errors as obtained by the output of the
 alignment procedure that was run at CERN Tier-0 to verify the efficiency of the alignment procedure on the track
reconstruction. The refit of tracks is performed also in this case.
 
\end{itemize}

Track reconstruction is based on the Kalman Filter formalism \cite{Kalman} for trajectory building, cleaning and 
smoothing steps and uses hits from pixel detector as seeds to provide initial trajectory candidates. 
Because of the misalignment the analysis requires to refit tracks with a misaligned tracker geometry.
Global efficiency of track reconstruction and track parameter resolutions for muons were
compared in all the cases. The association between simulated track and reconstructed tracks is performed
by comparing the corresponding track parameters at the closest approach point and choosing the pair which gives 
the minimum $\chi^2$ from the best fit procedure.

Events from CSA06 $Z\rightarrow \mu \mu $ sample were firstly skimmed by selecting events with 
 Hep MC muons from Z decay with pseudorapidity, $\eta$, in the tracker acceptance, $|\eta| < 2.55$, 
with transverse momentum larger than $5 \, \mathrm{GeV}/c^2$ and dimuons invariant mass in the following 
range aroung the Z peak: $50 < m_{\mu\mu}(\mathrm{GeV}/c^2) < 130$; the efficiency of the previous selection is 
between 50 and 60 \% mainly due to the cut on the acceptance, for a final statistics of 1 million events. 
The output files in RECOSIM format were needed for the subsequent analysis.


Jobs executing the misalignment analysis were submitted at Bari with CRAB\_1\_4\_0 in the LCG infrastructure.
A total of about 2.5 thousands jobs (45 at most in parallel) ran with a grid efficiency of 90 \% and an 
application efficiency of 80\%, by accessing detector position and errors from the offline database via 
frontier.


Some results of the misalignment analysis were summarized below. The global efficiency of track reconstruction of muons coming from
Z decay is shown in Fig.~\ref{eff} as a function of the pseudorapidity, $\eta$, in the tracker acceptance. 
In the case of a perfect geometry the global track reconstruction was not fully efficient over all the $\eta$ 
range because of the track associator algorithm itself which discards tracks with  $\chi^2$ of the fit larger 
than 25. The effect of misalignment is relevant in the short term scenario and causes a partial inefficiency of the
track reconstruction; that can be recovered if the intrinsic position resolution of the tracker
 detector is combined with the alignment uncertainties to make larger the error on the position of the
reconstructed hit (called alignment position error, APE) so improving the track fit at the
expense of a larger rate of fake tracks.
                                    

The transverse momentum resolution as a function of the transverse momentum is reported in Fig.~\ref{respt};
the degradation of the transverse momentum resolution at large $p_{T}$ because of the misalignment is in a factor
 between 2 and 3 with respect to the perfect geometry case. At low transverse momentum (less than few $\mathrm{GeV}/c$ 
the multiple scattering is the 
most important contribution to the resolution so the effect of misalignment is overwhelmed at all.
                                                                                    
The residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
tracker geometry and in short-term and long term misalignment scenarios is shown in Fig.~\ref{mz}; the $\sigma$ of 
the Gaussian fit of the residual distribution can be quoted as the Z mass resolution which is degraded by a factor 2 
because of the tracker misalignment in the short term scenario.
                                                                         
\begin{2figures}{hbt}
  \resizebox{\linewidth}{!}{\includegraphics{figs/Eff_eta}} &
  \resizebox{\linewidth}{!}{\includegraphics{figs/SigmapT_pT}} \\
  \caption{Global track reconstruction efficiency vs pseudorapidity for muons coming from Z decay in the case of perfect 
tracker geometry and in short-term and long term misalignment scenarios when the APE is not used.}
  \label{eff} &
  \caption{$P_{T}$ resolution vs $p_{T}$ in the case of perfect
tracker geometry and in short-term and long term misalignment scenarios.}
  \label{respt} \\
\end{2figures}


\begin{figure}[htb]
  \begin{center}
  \resizebox{0.7\linewidth}{!}{\includegraphics{figs/Residual_mZ_mu}} 
  \end{center}
  \caption{Residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
tracker geometry and in short-term and long term misalignment scenarios.}
  \label{mz} 
\end{figure}

% muon analyses

\input{wmunu.tex}

\input{dimuon.tex}

% electron analyses
\input{zee.tex}

\input{wmass.tex}


% tau analyses:
         
\input{z_2tau.tex}

\input{taumisid.tex}

\input{tau_validation.tex}

% Higgs
\input{hww_2l.tex}

\input{qqh_inv_zbkg.tex}

% min bias studies
\input{mbue.tex}

\input{ttbar_ana.tex}

% SUSY
\input{susybsm.tex}

%BSM analyses
%input{heep-analysis.tex}
Revision:	1.22
Committed:	Sat Jan 27 11:30:41 2007 UTC (18 years, 3 months ago) by acosta
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.21:	+2 -2 lines
Log Message:	more spelling, edits
#	User	Rev	Content
1	fisk	1.1	\section {Analysis Demonstrations}
2
3	acosta	1.19	A wide variety of physics analysis demonstrations were
4			prepared by the
5	acosta	1.15	physics groups for CSA06 in order to test the analysis workflow for
6	acosta	1.19	CSA06. The number of analyses is approximately 28, with nearly 70
7			active participants.
8			These demonstrations proved to be useful training
9			exercises for collaborators in the new software and computing tools as well.
10	acosta	1.15	The list of specialized calibration and
11			alignment streams produced at the Tier-0 and analyzed is discussed in
12			Section~\ref{sec:offlineswalca}. The list of general physics analysis
13			skims produced at the Tier-1 centres is covered in
14			Table~\ref{tab:tier1skim}. Table~\ref{tab:analyses} lists the physics
15			analyses that were conducted as part of CSA06. Details on these
16	acosta	1.20	analyses are contained in the following subsections.
17			%Unless noted,
18			%CRAB was used to submit the analysis jobs.
19	acosta	1.15
20
21			\begin{table}[phtb]
22			\centering
23			\caption{List of CSA06 analyses.}
24	acosta	1.19	\vspace{3mm}
25	acosta	1.15	\label{tab:analyses}
26	acosta	1.19	\begin{tabular}{\|l\|l\|l\|l\|}
27	acosta	1.15	\hline
28	acosta	1.19	Group & Analysis & People & Dataset \\ \hline
29			eg & ECAL isol. electron calib. & L.Agostino (CERN), P. Govoni (Milan), & AlCaReco \\
30			& & L.Malgeri (CERN), R.Ofierzynski (CERN) & \\
31			eg & ECAL Phi symmetry calib. & D.Futyan (IC) & minbias AlCaReco \\
32	acosta	1.20	eg & Z$\rightarrow ee$ reco. & P.Meridiani (Rome) & Zee AlCaReco \\
33	acosta	1.19	\hline
34			jm & HCAL Phi symmetry calib. & O.Kodolova (Moscow) & minbias AlCaReco \\
35			jm & HCAL isol. trk. calib. & M.Szleper (Northwestern), & minbias AlCaReco \\
36			& & S.Petrushanko (Moscow) & \\
37			jm & Jet calib. & R.Harris, M.Cardaci (FNAL) & Jets \\
38			\hline
39			tk & Partial Tracker alignment & L.Edera, F.Ronga, and
40			O.Buchmuller (CERN), & Zmumu AlcaReco \\
41			& & F.-P. Schilling (Karlsruhe), & \\
42			tk & Misalignments effects on track reco & N. De Filippis (Bari) & \\
43			\hline
44			mu & Muon alignment & J.Fernandez, P.Martinez,
45			& Muon AlCaReco \\
46			& & F.Matorras (Santander) & \\
47			mu & W$\rightarrow\mu\nu$ & M.Biasotto, U.Gasparini, M.Margoni, & EWK, Soft Muon skims \\
48			& & E.Torassa (Padova) & \\
49	acosta	1.17	mu & J/Psi and Z$\rightarrow\mu\mu$ & J.Alcaraz, J.Hernandez,
50	acosta	1.19	J.Caballero & EWK, Soft Muon skims \\
51			& & P.Garcia Abia (CIEMAT) & \\
52			mu & Z$\rightarrow\mu\mu$ and muon effic. & C.Liu and N.Neumeister (Purdue) & EWK skim \\
53			& & M.Schmitt (Northwestern) & \\
54			\hline
55	acosta	1.15	hg & Selection of Z$\rightarrow 2\tau$ &
56	acosta	1.19	K.Petridis (IC), A.Kalinowski (Warsaw) & EWK skims \\
57	acosta	1.15	hg & jet$\rightarrow \tau$ mis id &
58	acosta	1.19	F.Blekman (IC), C.Siamitros (Brunel) & Z+jet\\
59	acosta	1.15	hg & tau tagging efficiency from Z$\rightarrow 2\tau$ &
60	acosta	1.19	S.Gennai and G.Bagliesi (Pisa), & EWK skims \\
61			& & A.Goussiou and R.Vasques Sierra (FNAL) & \\
62	acosta	1.15	hg & tau HLT with pixel trigger using Z$\rightarrow 2\tau$ &
63	acosta	1.19	D.Kotlinski and P.Trueb (PSI) & EWK skims \\
64	acosta	1.17	hg & Background to H$\rightarrow WW\rightarrow \ell\ell$ &
65	acosta	1.19	F. Stoeckli (ETH) & TTbar \\
66	acosta	1.15	hg & Z+2Jet background to qqH, H$\rightarrow$inv &
67	acosta	1.19	J.Brooke and S.Metson (Bristol), & EWK skims \\
68			& & K. Mazumdar, S.Bansal (TIFR) & \\
69			\hline
70	acosta	1.20	sm & underlying event/minbias & L.Fano and F.Ambroglini (Pisa) &
71			m.b., Jets, D-Y skims \\
72	acosta	1.17	& & P.Bartalini and R.Field (Florida) & \\
73			& & F.Bechtel (Hamburg) & \\
74	acosta	1.19	sm & T-Tbar dilepton selection & I.Gonzalez-Caballero (Santander), & TTbar skim \\
75			& & J.Cuevas Maestro (Oviedo) & \\
76			sm & T-Tbar inclusive & I.Gonzalez-Caballero (Santander), & TTbar skims \\
77			& & J.Vizan (Oviedo) & \\
78			& & J.Heynick (Brussels) & \\
79			& & J.Vizan (Oviedo) & \\
80			sm & W mass & M.Malberti (Milan) & EWK skim \\
81			\hline
82			su & LM1 Jets + MET & M.Tytgat, M.Spiropulu (CERN) & Exotic, TTbar skims \\
83			su & Di-tau + MET & D.J.Mangeol (Strasbourg) & Exotic \\
84			su & LM1 b-tagging & R.Stringer (Riverside) & Exotic \\
85			su & Dijet mass & R.Harris, M.Cardaci (FNAL) & QCD, Exotic skims\\
86			su & High energy $e^+e^-$ & P.Vanlaer (Brussels),
87			D.Evans (Bristol), & Exotic, EWK skims \\
88			& & C.Shepherd-Themistocleous (RAL) & \\
89			su & Z$'\rightarrow \mu^+\mu^-$ & A.Lanyov, S.Shmatov (Dubna) & Exotic \\
90	acosta	1.15	\hline
91			\end{tabular}
92			\end{table}
93
94
95	ndefilip	1.3
96	fisk	1.1	\subsection {Calibration}
97
98	acosta	1.14	\label{sec:calib}
99
100	malgeri	1.6	\input{calib_ana.tex}
101
102	fpschill	1.5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
103
104			\subsection {Alignment}
105
106	acosta	1.14	\label{sec:align}
107
108	acosta	1.19	\subsubsection{Tracker Alignment}
109
110	fpschill	1.5	A tracker alignment CSA06 exercise was carried out with the goal to
111			demonstrate the full work- and dataflow of the alignment process.
112			The exercise followed closely the ideas and concepts
113			developed during the T0-RTAG~\cite{tier0rtag}.
114			The exercise comprised the following steps:
115			\begin{itemize}
116			\item Reading of alignment constants from the offline database during prompt
117			reconstruction;
118			\item Writing dedicated AlCaReco streams for alignment;
119			\item Defining a misalignment scenario and insertion of the corresponding
120			object into the offline database;
121			\item Running an alignment algorithm at the Tier-0;
122			\item Inserting the resulting alignment corrections into the database;
123			\item Running re-reconstruction at a Tier-1 centre reading this alignment
124			object;
125			\item Running analysis jobs in which ideal, misaligned
126			and aligned distributions are compared.
127			\end{itemize}
128
129			The steps regarding production of the dedicated AlCaReco streams
130			as well as enabling the prompt reconstruction to read alignment
131			constants from the offline database were already described in
132			section~\ref{sec:offlineswalca}. In the following, a short
133			summary of the exercise is given. For more details,
134			see~\cite{alcacsa06note}.
135
136			The following misalignment scenario was defined for this exercise: The
137			sensors of the Tracker Inner Barrel TIB as well as the Rods of the
138			Tracker Outer Barrel TOB were misaligned. Random shifts drawing from
139			a flat distribution in the range $\pm 100 \rm\ \mu m$ were applied in
140			$u$, $v$, $w$ (local coordinate system) for layers built from double
141			sided modules, and in $u$ (precise coordinate) only for layers built
142			from single sided modules. In addition, random rotations around all
143			three local coordinate axes of size $\pm 10 \rm\ mrad$ were applied to
144			the modules/rods of both single and double sided TIB and TOB layers.
145			The pixel detector was kept fixed
146			in order to define a reference system. In addition, the outermost TOB
147			layer was also kept fixed in order to improve the convergence. This
148			misalignment scenario was inserted as an alignment object into the
149			offline database. In addition, another object corresponding to the
150			ideal tracker geometry was inserted, to be used during prompt
151			reconstruction.
152
153			\begin{figure}
154			\centering
155	acosta	1.18	\includegraphics[width=0.8\linewidth]{figs/csa06_convergence}
156	fpschill	1.5	\caption{Alignment exercise: The convergence of the alignment
157			algorithm in $\Delta u, \Delta v, \Delta w$ is shown for double sided
158			TIB modules as a function of the iteration number (left), as well as
159			projected for initial misalignment (labeled ``after 0 Iterations'')
160			and after 4, 7 and 10 iterations (right). }
161			\label{fig:alignment_convergence}
162			\end{figure}
163
164			The alignment was performed running the HIP alignment
165			algorithm~\cite{hipnote} as implemented in CMSSW\_1\_0\_6 over approx.
166			$1 \rm\ M$ AlCaReco $Z^0\rightarrow\mu^+\mu^-$ events produced during
167			prompt reconstruction at the Tier-0, reading the above mentioned
168			misalignment object from the offline database. The algorithm was run
169			on 20 dedicated CPUs in parallel at CERN, iterating 10 times over the
170			data sample. The result of the alignment was obtained in less than $5
171			\rm\ h$, and the corresponding tracker alignment object
172			was inserted into the database. Figure~\ref{fig:alignment_convergence}
173			illustrates the convergence of the alignment for the double sided TIB
174			sensors.
175
176			\begin{figure}
177			\centering
178	acosta	1.18	\includegraphics[width=0.8\linewidth]{figs/csa06_aliexercise}
179	fpschill	1.5	\caption{Invariant mass distribution from $Z^0\to \mu^+ \mu^-$ events,
180			obtained from events produced by the prompt reconstruction at
181			the Tier-0 (``ideal''), from events processed with misalignment
182			as used as input for the alignment algorithm (``misaligned'') and
183			from events re-reconstructed at a Tier-1 centre (PIC) using the alignment
184			constants derived from the alignment algorithm (``realigned'').
185			}
186			\label{fig:aliexercise}
187			\end{figure}
188
189			Once the alignment and calibration constants were inserted in the
190			database, they were deployed to the Tier-1/2 centres via Frontier.
191			Subsequently, re-reconstruction of some of the CSA06 datasets was
192			launched at various Tier-1 centres. For instance, the
193			$Z^0\rightarrow\mu^+\mu^-$ data set was re-reconstructed at PIC
194			(Barcelona) using the new alignment object. In order to demonstrate
195			the final missing piece of the workflow, grid analysis jobs were
196			submitted to PIC to process these re-reconstructed
197	acosta	1.22	$Z^0\rightarrow\mu^+\mu^-$ events. The reconstructed invariant dimuon
198	fpschill	1.5	mass is presented in Figure~\ref{fig:aliexercise} for three cases:
199			\begin{itemize}
200			\item Using the ideal geometry, reading the RECO produced during prompt
201			reconstruction;
202			\item Using the misaligned geometry, reading the AlCaReco and the misalignment
203			scenario database object;
204			\item Using the realigned geometry, reading the events re-reconstructed
205			with the alignment database object.
206			\end{itemize}
207			As can be seen, the invariant mass resolution is degraded in the case
208			of misalignment. After the alignment algorithm has corrected the
209			tracker geometry, the resolution is recovered close to the original
210			value. This demonstrates that the work- and dataflow of the full
211			alignment was successfully carried out.
212
213
214	acosta	1.16	\input{muonalignment.tex}
215
216	fpschill	1.5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
217	fisk	1.1
218			\subsection {Physics Analysis Exercises}
219
220	ndefilip	1.2	\subsubsection {Effect of tracker misalignment on track reconstruction performances}
221
222	ndefilip	1.3	The alignment uncertainties of the CMS Tracker detector, made of a huge amount of independent silicon sensors with an excellent position resolution, affect the performances of the track reconstruction and track parameters measurement.
223			The analysis exercise performed by the team at Bari during the CSA06 had the purpose of study the effect of the CMS tracker misalignment on the performances of the track reconstruction \cite{misalignment}. Realistic estimates for the expected displacements of the tracking systems were supplied in different scenarios as specified in the following:
224
225			\begin{itemize}
226			\item the ideal scenario with a perfect tracker geometry;
227			\item the short term misalignment scenario supposed to reproduce the mis-alignment conditions during the first
228			data taking when the uncertainties on the position of the sub-structures of the CMS
229			tracker will be between $10 \, \mu$ for pixel detectors and $400 \, \mu$ for microstrip silicon detectors in
230			the endcaps. Detector position and errors are read
231			from the offline database at CERN by caching the needed information locally via frontier/squid
232	ndefilip	1.7	software.
233	ndefilip	1.3
234			\item the long term scenario when the alignment uncertainties are supposed to be a factor 10 smaller because of the
235	acosta	1.21	improvement obtained by using alignment algorithms with a high statistics of tracks.
236	ndefilip	1.3
237			\item the CSA06 aligned scenario by using the tracker module position and errors as obtained by the output of the
238			alignment procedure that was run at CERN Tier-0 to verify the efficiency of the alignment procedure on the track
239			reconstruction. The refit of tracks is performed also in this case.
240
241			\end{itemize}
242
243	ndefilip	1.4	Track reconstruction is based on the Kalman Filter formalism \cite{Kalman} for trajectory building, cleaning and
244			smoothing steps and uses hits from pixel detector as seeds to provide initial trajectory candidates.
245			Because of the misalignment the analysis requires to refit tracks with a misaligned tracker geometry.
246	acosta	1.21	Global efficiency of track reconstruction and track parameter resolutions for muons were
247			compared in all the cases. The association between simulated track and reconstructed tracks is performed
248	ndefilip	1.4	by comparing the corresponding track parameters at the closest approach point and choosing the pair which gives
249			the minimum $\chi^2$ from the best fit procedure.
250	ndefilip	1.3
251	ndefilip	1.4	Events from CSA06 $Z\rightarrow \mu \mu $ sample were firstly skimmed by selecting events with
252			Hep MC muons from Z decay with pseudorapidity, $\eta$, in the tracker acceptance, $\|\eta\| < 2.55$,
253	acosta	1.22	with transverse momentum larger than $5 \, \mathrm{GeV}/c^2$ and dimuons invariant mass in the following
254	ndefilip	1.4	range aroung the Z peak: $50 < m_{\mu\mu}(\mathrm{GeV}/c^2) < 130$; the efficiency of the previous selection is
255			between 50 and 60 \% mainly due to the cut on the acceptance, for a final statistics of 1 million events.
256			The output files in RECOSIM format were needed for the subsequent analysis.
257	ndefilip	1.3
258
259	ndefilip	1.4	Jobs executing the misalignment analysis were submitted at Bari with CRAB\_1\_4\_0 in the LCG infrastructure.
260			A total of about 2.5 thousands jobs (45 at most in parallel) ran with a grid efficiency of 90 \% and an
261			application efficiency of 80\%, by accessing detector position and errors from the offline database via
262			frontier.
263	ndefilip	1.3
264	ndefilip	1.4
265			Some results of the misalignment analysis were summarized below. The global efficiency of track reconstruction of muons coming from
266			Z decay is shown in Fig.~\ref{eff} as a function of the pseudorapidity, $\eta$, in the tracker acceptance.
267			In the case of a perfect geometry the global track reconstruction was not fully efficient over all the $\eta$
268	ndefilip	1.12	range because of the track associator algorithm itself which discards tracks with $\chi^2$ of the fit larger
269	ndefilip	1.4	than 25. The effect of misalignment is relevant in the short term scenario and causes a partial inefficiency of the
270			track reconstruction; that can be recovered if the intrinsic position resolution of the tracker
271			detector is combined with the alignment uncertainties to make larger the error on the position of the
272			reconstructed hit (called alignment position error, APE) so improving the track fit at the
273	acosta	1.21	expense of a larger rate of fake tracks.
274	ndefilip	1.4
275
276	acosta	1.21	The transverse momentum resolution as a function of the transverse momentum is reported in Fig.~\ref{respt};
277			the degradation of the transverse momentum resolution at large $p_{T}$ because of the misalignment is in a factor
278	ndefilip	1.4	between 2 and 3 with respect to the perfect geometry case. At low transverse momentum (less than few $\mathrm{GeV}/c$
279			the multiple scattering is the
280			most important contribution to the resolution so the effect of misalignment is overwhelmed at all.
281
282			The residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
283			tracker geometry and in short-term and long term misalignment scenarios is shown in Fig.~\ref{mz}; the $\sigma$ of
284	acosta	1.21	the Gaussian fit of the residual distribution can be quoted as the Z mass resolution which is degraded by a factor 2
285	ndefilip	1.4	because of the tracker misalignment in the short term scenario.
286
287			\begin{2figures}{hbt}
288	malgeri	1.6	\resizebox{\linewidth}{!}{\includegraphics{figs/Eff_eta}} &
289			\resizebox{\linewidth}{!}{\includegraphics{figs/SigmapT_pT}} \\
290	acosta	1.21	\caption{Global track reconstruction efficiency vs pseudorapidity for muons coming from Z decay in the case of perfect
291	ndefilip	1.3	tracker geometry and in short-term and long term misalignment scenarios when the APE is not used.}
292			\label{eff} &
293	ndefilip	1.4	\caption{$P_{T}$ resolution vs $p_{T}$ in the case of perfect
294			tracker geometry and in short-term and long term misalignment scenarios.}
295			\label{respt} \\
296			\end{2figures}
297
298
299			\begin{figure}[htb]
300			\begin{center}
301	malgeri	1.6	\resizebox{0.7\linewidth}{!}{\includegraphics{figs/Residual_mZ_mu}}
302	ndefilip	1.4	\end{center}
303	ndefilip	1.3	\caption{Residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
304			tracker geometry and in short-term and long term misalignment scenarios.}
305	ndefilip	1.4	\label{mz}
306			\end{figure}
307
308	acosta	1.19	% muon analyses
309
310			\input{wmunu.tex}
311
312			\input{dimuon.tex}
313
314			% electron analyses
315			\input{zee.tex}
316
317			\input{wmass.tex}
318
319	acosta	1.8
320			% tau analyses:
321	ndefilip	1.4
322	acosta	1.8	\input{z_2tau.tex}
323
324			\input{taumisid.tex}
325
326			\input{tau_validation.tex}
327
328	acosta	1.19	% Higgs
329			\input{hww_2l.tex}
330	acosta	1.9
331	acosta	1.19	\input{qqh_inv_zbkg.tex}
332	acosta	1.9
333	acosta	1.19	% min bias studies
334			\input{mbue.tex}
335	acosta	1.13
336	acosta	1.19	\input{ttbar_ana.tex}
337	meridian	1.11
338	acosta	1.19	% SUSY
339			\input{susybsm.tex}
340	acosta	1.17
341			%BSM analyses
342	acosta	1.19	%input{heep-analysis.tex}