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 & ECAL Z$\rightarrow ee$ calib.  & 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 (Perugia) &
minbias \\
   & & 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 di-muon
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 aligmnent 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 recostruction and track parameter resolutions for muons were
compared in all the cases. The association between simulated track and reconstruted 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 di-muons 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
expence of a larger rate of fake tracks.
                                    

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