COMP/CSA06DOC/analysisdemo.tex

\clearpage
\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 analyses conducted on the CSA-produced data
samples numbered approximately 30, 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  & M.Abbrescia, G.Cuscela,
N.De Filippis,  &  Zmumu skim \\
 & & G.Donvito, G.Maggi, S.My (Bari)  &   \\
\hline
mu & Muon alignment  & J.Fernandez, P.Martinez, 
  & Muon AlCaReco  \\
 &  &  F.Matorras (IFCA, 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)  &  \\
mu & Dimuon reconstruction & B.Kim (Florida)  & SoftMuon, minbias \\
\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 (IFCA, Santander),  & TTbar skim \\
 &  & J.Cuevas Maestro (Oviedo) &  \\
sm & T-Tbar inclusive & I.Gonzalez-Caballero (IFCA, 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 electron cleanup & F.Moortgat, L.Pape & 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$'\rightarrow \mu^+\mu^-$  & M.Tytgat, M.Spiropulu (CERN) & Exotic \\
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 approximately 
$1 \rm\ M$ 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 as described in Section~\ref{sec:rereco}. 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 purpose of the analysis exercise performed by the team at Bari
during the CSA06 was to study the effect of the CMS tracker misalignment on
the performance 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 representing the
  typical 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 $10 \, \mu$m for pixel detectors and $400 \, \mu$m 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 expected 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 (Section~\ref{sec:align}) that was run at the
 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 a transverse momentum larger than $5 \, \mathrm{GeV}/c^2$ and a dimuon invariant mass in the following 
range aroung the Z peak: $50 < m_{\mu\mu}(\mathrm{GeV}/c^2) < 130$. The efficiency of this selection is 
between 50 and 60\% mainly due to the cut on the acceptance, for a
 final sample 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 are 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 is 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 observed ib 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 the alignment position error, or  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 negligible.
                                                                                    
The residual of the Z mass obtained as the invariant mass of muons coming from Z decays for the case of perfect
tracker geometry and for the 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 decayd for the case of perfect
tracker geometry and for the 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:	1.26
Committed:	Fri Feb 2 03:15:46 2007 UTC (18 years, 2 months ago) by acosta
Content type:	application/x-tex
Branch:	MAIN
Changes since 1.25:	+7 -4 lines
Log Message:	suggestions from others
#	User	Rev	Content
1	acosta	1.24	\clearpage
2	fisk	1.1	\section {Analysis Demonstrations}
3
4	acosta	1.19	A wide variety of physics analysis demonstrations were
5			prepared by the
6	acosta	1.15	physics groups for CSA06 in order to test the analysis workflow for
7	acosta	1.24	CSA06. The analyses conducted on the CSA-produced data
8			samples numbered approximately 30, with nearly 70
9	acosta	1.19	active participants.
10			These demonstrations proved to be useful training
11			exercises for collaborators in the new software and computing tools as well.
12	acosta	1.15	The list of specialized calibration and
13			alignment streams produced at the Tier-0 and analyzed is discussed in
14			Section~\ref{sec:offlineswalca}. The list of general physics analysis
15			skims produced at the Tier-1 centres is covered in
16			Table~\ref{tab:tier1skim}. Table~\ref{tab:analyses} lists the physics
17			analyses that were conducted as part of CSA06. Details on these
18	acosta	1.20	analyses are contained in the following subsections.
19			%Unless noted,
20			%CRAB was used to submit the analysis jobs.
21	acosta	1.15
22
23			\begin{table}[phtb]
24			\centering
25			\caption{List of CSA06 analyses.}
26	acosta	1.19	\vspace{3mm}
27	acosta	1.15	\label{tab:analyses}
28	acosta	1.19	\begin{tabular}{\|l\|l\|l\|l\|}
29	acosta	1.15	\hline
30	acosta	1.19	Group & Analysis & People & Dataset \\ \hline
31			eg & ECAL isol. electron calib. & L.Agostino (CERN), P. Govoni (Milan), & AlCaReco \\
32			& & L.Malgeri (CERN), R.Ofierzynski (CERN) & \\
33			eg & ECAL Phi symmetry calib. & D.Futyan (IC) & minbias AlCaReco \\
34	acosta	1.20	eg & Z$\rightarrow ee$ reco. & P.Meridiani (Rome) & Zee AlCaReco \\
35	acosta	1.19	\hline
36			jm & HCAL Phi symmetry calib. & O.Kodolova (Moscow) & minbias AlCaReco \\
37			jm & HCAL isol. trk. calib. & M.Szleper (Northwestern), & minbias AlCaReco \\
38			& & S.Petrushanko (Moscow) & \\
39			jm & Jet calib. & R.Harris, M.Cardaci (FNAL) & Jets \\
40			\hline
41			tk & Partial Tracker alignment & L.Edera, F.Ronga, and
42			O.Buchmuller (CERN), & Zmumu AlcaReco \\
43			& & F.-P. Schilling (Karlsruhe), & \\
44	acosta	1.26	tk & Misalignments effects on track reco & M.Abbrescia, G.Cuscela,
45			N.De Filippis, & Zmumu skim \\
46			& & G.Donvito, G.Maggi, S.My (Bari) & \\
47	acosta	1.19	\hline
48			mu & Muon alignment & J.Fernandez, P.Martinez,
49			& Muon AlCaReco \\
50	acosta	1.26	& & F.Matorras (IFCA, Santander) & \\
51	acosta	1.19	mu & W$\rightarrow\mu\nu$ & M.Biasotto, U.Gasparini, M.Margoni, & EWK, Soft Muon skims \\
52			& & E.Torassa (Padova) & \\
53	acosta	1.17	mu & J/Psi and Z$\rightarrow\mu\mu$ & J.Alcaraz, J.Hernandez,
54	acosta	1.19	J.Caballero & EWK, Soft Muon skims \\
55			& & P.Garcia Abia (CIEMAT) & \\
56			mu & Z$\rightarrow\mu\mu$ and muon effic. & C.Liu and N.Neumeister (Purdue) & EWK skim \\
57			& & M.Schmitt (Northwestern) & \\
58	acosta	1.23	mu & Dimuon reconstruction & B.Kim (Florida) & SoftMuon, minbias \\
59	acosta	1.19	\hline
60	acosta	1.15	hg & Selection of Z$\rightarrow 2\tau$ &
61	acosta	1.19	K.Petridis (IC), A.Kalinowski (Warsaw) & EWK skims \\
62	acosta	1.15	hg & jet$\rightarrow \tau$ mis id &
63	acosta	1.19	F.Blekman (IC), C.Siamitros (Brunel) & Z+jet\\
64	acosta	1.15	hg & tau tagging efficiency from Z$\rightarrow 2\tau$ &
65	acosta	1.19	S.Gennai and G.Bagliesi (Pisa), & EWK skims \\
66			& & A.Goussiou and R.Vasques Sierra (FNAL) & \\
67	acosta	1.15	hg & tau HLT with pixel trigger using Z$\rightarrow 2\tau$ &
68	acosta	1.19	D.Kotlinski and P.Trueb (PSI) & EWK skims \\
69	acosta	1.17	hg & Background to H$\rightarrow WW\rightarrow \ell\ell$ &
70	acosta	1.19	F. Stoeckli (ETH) & TTbar \\
71	acosta	1.15	hg & Z+2Jet background to qqH, H$\rightarrow$inv &
72	acosta	1.19	J.Brooke and S.Metson (Bristol), & EWK skims \\
73			& & K. Mazumdar, S.Bansal (TIFR) & \\
74			\hline
75	acosta	1.20	sm & underlying event/minbias & L.Fano and F.Ambroglini (Pisa) &
76			m.b., Jets, D-Y skims \\
77	acosta	1.17	& & P.Bartalini and R.Field (Florida) & \\
78			& & F.Bechtel (Hamburg) & \\
79	acosta	1.26	sm & T-Tbar dilepton selection & I.Gonzalez-Caballero (IFCA, Santander), & TTbar skim \\
80	acosta	1.19	& & J.Cuevas Maestro (Oviedo) & \\
81	acosta	1.26	sm & T-Tbar inclusive & I.Gonzalez-Caballero (IFCA, Santander), & TTbar skims \\
82	acosta	1.19	& & J.Vizan (Oviedo) & \\
83			& & J.Heynick (Brussels) & \\
84			& & J.Vizan (Oviedo) & \\
85			sm & W mass & M.Malberti (Milan) & EWK skim \\
86			\hline
87			su & LM1 Jets + MET & M.Tytgat, M.Spiropulu (CERN) & Exotic, TTbar skims \\
88			su & Di-tau + MET & D.J.Mangeol (Strasbourg) & Exotic \\
89	acosta	1.25	su & LM1 electron cleanup & F.Moortgat, L.Pape & Exotic \\
90	acosta	1.19	su & LM1 b-tagging & R.Stringer (Riverside) & Exotic \\
91			su & Dijet mass & R.Harris, M.Cardaci (FNAL) & QCD, Exotic skims\\
92			su & High energy $e^+e^-$ & P.Vanlaer (Brussels),
93			D.Evans (Bristol), & Exotic, EWK skims \\
94			& & C.Shepherd-Themistocleous (RAL) & \\
95	acosta	1.26	su & Z$'\rightarrow \mu^+\mu^-$ & M.Tytgat, M.Spiropulu (CERN) & Exotic \\
96	acosta	1.19	su & Z$'\rightarrow \mu^+\mu^-$ & A.Lanyov, S.Shmatov (Dubna) & Exotic \\
97	acosta	1.15	\hline
98			\end{tabular}
99			\end{table}
100
101
102	ndefilip	1.3
103	fisk	1.1	\subsection {Calibration}
104
105	acosta	1.14	\label{sec:calib}
106
107	malgeri	1.6	\input{calib_ana.tex}
108
109	fpschill	1.5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
110
111			\subsection {Alignment}
112
113	acosta	1.14	\label{sec:align}
114
115	acosta	1.19	\subsubsection{Tracker Alignment}
116
117	fpschill	1.5	A tracker alignment CSA06 exercise was carried out with the goal to
118			demonstrate the full work- and dataflow of the alignment process.
119			The exercise followed closely the ideas and concepts
120			developed during the T0-RTAG~\cite{tier0rtag}.
121			The exercise comprised the following steps:
122			\begin{itemize}
123			\item Reading of alignment constants from the offline database during prompt
124			reconstruction;
125			\item Writing dedicated AlCaReco streams for alignment;
126			\item Defining a misalignment scenario and insertion of the corresponding
127			object into the offline database;
128			\item Running an alignment algorithm at the Tier-0;
129			\item Inserting the resulting alignment corrections into the database;
130			\item Running re-reconstruction at a Tier-1 centre reading this alignment
131			object;
132			\item Running analysis jobs in which ideal, misaligned
133			and aligned distributions are compared.
134			\end{itemize}
135
136			The steps regarding production of the dedicated AlCaReco streams
137			as well as enabling the prompt reconstruction to read alignment
138			constants from the offline database were already described in
139			section~\ref{sec:offlineswalca}. In the following, a short
140			summary of the exercise is given. For more details,
141			see~\cite{alcacsa06note}.
142
143			The following misalignment scenario was defined for this exercise: The
144			sensors of the Tracker Inner Barrel TIB as well as the Rods of the
145			Tracker Outer Barrel TOB were misaligned. Random shifts drawing from
146			a flat distribution in the range $\pm 100 \rm\ \mu m$ were applied in
147			$u$, $v$, $w$ (local coordinate system) for layers built from double
148			sided modules, and in $u$ (precise coordinate) only for layers built
149			from single sided modules. In addition, random rotations around all
150			three local coordinate axes of size $\pm 10 \rm\ mrad$ were applied to
151			the modules/rods of both single and double sided TIB and TOB layers.
152			The pixel detector was kept fixed
153			in order to define a reference system. In addition, the outermost TOB
154			layer was also kept fixed in order to improve the convergence. This
155			misalignment scenario was inserted as an alignment object into the
156			offline database. In addition, another object corresponding to the
157			ideal tracker geometry was inserted, to be used during prompt
158			reconstruction.
159
160			\begin{figure}
161			\centering
162	acosta	1.18	\includegraphics[width=0.8\linewidth]{figs/csa06_convergence}
163	fpschill	1.5	\caption{Alignment exercise: The convergence of the alignment
164			algorithm in $\Delta u, \Delta v, \Delta w$ is shown for double sided
165			TIB modules as a function of the iteration number (left), as well as
166			projected for initial misalignment (labeled ``after 0 Iterations'')
167			and after 4, 7 and 10 iterations (right). }
168			\label{fig:alignment_convergence}
169			\end{figure}
170
171			The alignment was performed running the HIP alignment
172	acosta	1.24	algorithm~\cite{hipnote} as implemented in CMSSW\_1\_0\_6 over approximately
173	fpschill	1.5	$1 \rm\ M$ AlCaReco $Z^0\rightarrow\mu^+\mu^-$ events produced during
174			prompt reconstruction at the Tier-0, reading the above mentioned
175			misalignment object from the offline database. The algorithm was run
176			on 20 dedicated CPUs in parallel at CERN, iterating 10 times over the
177			data sample. The result of the alignment was obtained in less than $5
178			\rm\ h$, and the corresponding tracker alignment object
179			was inserted into the database. Figure~\ref{fig:alignment_convergence}
180			illustrates the convergence of the alignment for the double sided TIB
181			sensors.
182
183			\begin{figure}
184			\centering
185	acosta	1.18	\includegraphics[width=0.8\linewidth]{figs/csa06_aliexercise}
186	fpschill	1.5	\caption{Invariant mass distribution from $Z^0\to \mu^+ \mu^-$ events,
187			obtained from events produced by the prompt reconstruction at
188			the Tier-0 (``ideal''), from events processed with misalignment
189			as used as input for the alignment algorithm (``misaligned'') and
190			from events re-reconstructed at a Tier-1 centre (PIC) using the alignment
191			constants derived from the alignment algorithm (``realigned'').
192			}
193			\label{fig:aliexercise}
194			\end{figure}
195
196			Once the alignment and calibration constants were inserted in the
197			database, they were deployed to the Tier-1/2 centres via Frontier.
198			Subsequently, re-reconstruction of some of the CSA06 datasets was
199	acosta	1.24	launched at various Tier-1 centres as described in Section~\ref{sec:rereco}. For instance, the
200	fpschill	1.5	$Z^0\rightarrow\mu^+\mu^-$ data set was re-reconstructed at PIC
201			(Barcelona) using the new alignment object. In order to demonstrate
202			the final missing piece of the workflow, grid analysis jobs were
203			submitted to PIC to process these re-reconstructed
204	acosta	1.22	$Z^0\rightarrow\mu^+\mu^-$ events. The reconstructed invariant dimuon
205	fpschill	1.5	mass is presented in Figure~\ref{fig:aliexercise} for three cases:
206			\begin{itemize}
207			\item Using the ideal geometry, reading the RECO produced during prompt
208			reconstruction;
209			\item Using the misaligned geometry, reading the AlCaReco and the misalignment
210			scenario database object;
211			\item Using the realigned geometry, reading the events re-reconstructed
212			with the alignment database object.
213			\end{itemize}
214			As can be seen, the invariant mass resolution is degraded in the case
215			of misalignment. After the alignment algorithm has corrected the
216			tracker geometry, the resolution is recovered close to the original
217			value. This demonstrates that the work- and dataflow of the full
218			alignment was successfully carried out.
219
220
221	acosta	1.16	\input{muonalignment.tex}
222
223	fpschill	1.5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
224	fisk	1.1
225			\subsection {Physics Analysis Exercises}
226
227	ndefilip	1.2	\subsubsection {Effect of tracker misalignment on track reconstruction performances}
228
229	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.
230	acosta	1.24	The purpose of the analysis exercise performed by the team at Bari
231			during the CSA06 was to study the effect of the CMS tracker misalignment on
232			the performance of the track reconstruction
233			\cite{misalignment}. Realistic estimates for the expected
234			displacements of the tracking systems were supplied in different
235			scenarios as specified in the following:
236	ndefilip	1.3
237			\begin{itemize}
238			\item the ideal scenario with a perfect tracker geometry;
239	acosta	1.24	\item the short term misalignment scenario representing the
240			typical mis-alignment conditions during the first
241			data-taking when the uncertainties on the position of the sub-structures of the CMS
242			tracker will be between $10 \, \mu$m for pixel detectors and $400 \, \mu$m for microstrip silicon detectors in
243	ndefilip	1.3	the endcaps. Detector position and errors are read
244			from the offline database at CERN by caching the needed information locally via frontier/squid
245	ndefilip	1.7	software.
246	ndefilip	1.3
247	acosta	1.24	\item the long term scenario when the alignment uncertainties are expected to be a factor 10 smaller because of the
248	acosta	1.21	improvement obtained by using alignment algorithms with a high statistics of tracks.
249	ndefilip	1.3
250			\item the CSA06 aligned scenario by using the tracker module position and errors as obtained by the output of the
251	acosta	1.24	alignment procedure (Section~\ref{sec:align}) that was run at the
252			CERN Tier-0 to verify the efficiency of the alignment procedure on
253			the track
254	ndefilip	1.3	reconstruction. The refit of tracks is performed also in this case.
255
256			\end{itemize}
257
258	ndefilip	1.4	Track reconstruction is based on the Kalman Filter formalism \cite{Kalman} for trajectory building, cleaning and
259			smoothing steps and uses hits from pixel detector as seeds to provide initial trajectory candidates.
260	acosta	1.24	Because of the misalignment, the analysis requires to refit tracks with a misaligned tracker geometry.
261	acosta	1.21	Global efficiency of track reconstruction and track parameter resolutions for muons were
262			compared in all the cases. The association between simulated track and reconstructed tracks is performed
263	ndefilip	1.4	by comparing the corresponding track parameters at the closest approach point and choosing the pair which gives
264			the minimum $\chi^2$ from the best fit procedure.
265	ndefilip	1.3
266	ndefilip	1.4	Events from CSA06 $Z\rightarrow \mu \mu $ sample were firstly skimmed by selecting events with
267			Hep MC muons from Z decay with pseudorapidity, $\eta$, in the tracker acceptance, $\|\eta\| < 2.55$,
268	acosta	1.24	with a transverse momentum larger than $5 \, \mathrm{GeV}/c^2$ and a dimuon invariant mass in the following
269			range aroung the Z peak: $50 < m_{\mu\mu}(\mathrm{GeV}/c^2) < 130$. The efficiency of this selection is
270			between 50 and 60\% mainly due to the cut on the acceptance, for a
271			final sample of 1 million events.
272	ndefilip	1.4	The output files in RECOSIM format were needed for the subsequent analysis.
273	ndefilip	1.3
274
275	ndefilip	1.4	Jobs executing the misalignment analysis were submitted at Bari with CRAB\_1\_4\_0 in the LCG infrastructure.
276	acosta	1.24	A total of about 2.5 thousands jobs (45 at most in parallel) ran with a grid efficiency of 90\% and an
277	ndefilip	1.4	application efficiency of 80\%, by accessing detector position and errors from the offline database via
278			frontier.
279	ndefilip	1.3
280	ndefilip	1.4
281	acosta	1.24	Some results of the misalignment analysis are summarized below. The global efficiency of track reconstruction of muons coming from
282	ndefilip	1.4	Z decay is shown in Fig.~\ref{eff} as a function of the pseudorapidity, $\eta$, in the tracker acceptance.
283	acosta	1.24	In the case of a perfect geometry the global track reconstruction is not fully efficient over all the $\eta$
284			range because of the track associator algorithm itself, which discards tracks with $\chi^2$ of the fit larger
285			than 25. The effect of misalignment is relevant observed ib the short term scenario and causes a partial inefficiency of the
286			track reconstruction that can be recovered if the intrinsic position resolution of the tracker
287	ndefilip	1.4	detector is combined with the alignment uncertainties to make larger the error on the position of the
288	acosta	1.24	reconstructed hit (called the alignment position error, or APE) so improving the track fit at the
289	acosta	1.21	expense of a larger rate of fake tracks.
290	ndefilip	1.4
291
292	acosta	1.21	The transverse momentum resolution as a function of the transverse momentum is reported in Fig.~\ref{respt};
293			the degradation of the transverse momentum resolution at large $p_{T}$ because of the misalignment is in a factor
294	ndefilip	1.4	between 2 and 3 with respect to the perfect geometry case. At low transverse momentum (less than few $\mathrm{GeV}/c$
295			the multiple scattering is the
296	acosta	1.24	most important contribution to the resolution so the effect of
297			misalignment is negligible.
298	ndefilip	1.4
299	acosta	1.24	The residual of the Z mass obtained as the invariant mass of muons coming from Z decays for the case of perfect
300			tracker geometry and for the short-term and long term misalignment scenarios is shown in Fig.~\ref{mz}; the $\sigma$ of
301	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
302	ndefilip	1.4	because of the tracker misalignment in the short term scenario.
303
304			\begin{2figures}{hbt}
305	malgeri	1.6	\resizebox{\linewidth}{!}{\includegraphics{figs/Eff_eta}} &
306			\resizebox{\linewidth}{!}{\includegraphics{figs/SigmapT_pT}} \\
307	acosta	1.21	\caption{Global track reconstruction efficiency vs pseudorapidity for muons coming from Z decay in the case of perfect
308	ndefilip	1.3	tracker geometry and in short-term and long term misalignment scenarios when the APE is not used.}
309			\label{eff} &
310	acosta	1.24	\caption{$p_{T}$ resolution vs $p_{T}$ in the case of perfect
311	ndefilip	1.4	tracker geometry and in short-term and long term misalignment scenarios.}
312			\label{respt} \\
313			\end{2figures}
314
315
316			\begin{figure}[htb]
317			\begin{center}
318	malgeri	1.6	\resizebox{0.7\linewidth}{!}{\includegraphics{figs/Residual_mZ_mu}}
319	ndefilip	1.4	\end{center}
320	acosta	1.24	\caption{Residual of Z mass obtained as the invariant mass of muons coming from Z decayd for the case of perfect
321			tracker geometry and for the short-term and long term misalignment scenarios.}
322	ndefilip	1.4	\label{mz}
323			\end{figure}
324
325	acosta	1.19	% muon analyses
326
327			\input{wmunu.tex}
328
329			\input{dimuon.tex}
330
331			% electron analyses
332			\input{zee.tex}
333
334			\input{wmass.tex}
335
336	acosta	1.8
337			% tau analyses:
338	ndefilip	1.4
339	acosta	1.8	\input{z_2tau.tex}
340
341			\input{taumisid.tex}
342
343			\input{tau_validation.tex}
344
345	acosta	1.19	% Higgs
346			\input{hww_2l.tex}
347	acosta	1.9
348	acosta	1.19	\input{qqh_inv_zbkg.tex}
349	acosta	1.9
350	acosta	1.19	% min bias studies
351			\input{mbue.tex}
352	acosta	1.13
353	acosta	1.19	\input{ttbar_ana.tex}
354	meridian	1.11
355	acosta	1.19	% SUSY
356			\input{susybsm.tex}
357	acosta	1.17
358			%BSM analyses
359	acosta	1.19	%input{heep-analysis.tex}