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fisk |
1.1 |
\section {Analysis Demonstrations}
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ndefilip |
1.3 |
Different physics groups prepared some analysis codes to extract
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physics results by running on skimmed files at Tier-2s.
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fisk |
1.1 |
\subsection {Calibration}
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acosta |
1.14 |
\label{sec:calib}
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malgeri |
1.6 |
\input{calib_ana.tex}
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fpschill |
1.5 |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\subsection {Alignment}
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acosta |
1.14 |
\label{sec:align}
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fpschill |
1.5 |
A tracker alignment CSA06 exercise was carried out with the goal to
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demonstrate the full work- and dataflow of the alignment process.
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The exercise followed closely the ideas and concepts
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developed during the T0-RTAG~\cite{tier0rtag}.
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The exercise comprised the following steps:
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\begin{itemize}
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\item Reading of alignment constants from the offline database during prompt
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reconstruction;
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\item Writing dedicated AlCaReco streams for alignment;
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\item Defining a misalignment scenario and insertion of the corresponding
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object into the offline database;
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\item Running an alignment algorithm at the Tier-0;
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\item Inserting the resulting alignment corrections into the database;
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\item Running re-reconstruction at a Tier-1 centre reading this alignment
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object;
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\item Running analysis jobs in which ideal, misaligned
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and aligned distributions are compared.
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\end{itemize}
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The steps regarding production of the dedicated AlCaReco streams
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as well as enabling the prompt reconstruction to read alignment
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constants from the offline database were already described in
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section~\ref{sec:offlineswalca}. In the following, a short
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summary of the exercise is given. For more details,
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see~\cite{alcacsa06note}.
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The following misalignment scenario was defined for this exercise: The
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sensors of the Tracker Inner Barrel TIB as well as the Rods of the
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Tracker Outer Barrel TOB were misaligned. Random shifts drawing from
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a flat distribution in the range $\pm 100 \rm\ \mu m$ were applied in
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$u$, $v$, $w$ (local coordinate system) for layers built from double
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sided modules, and in $u$ (precise coordinate) only for layers built
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from single sided modules. In addition, random rotations around all
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three local coordinate axes of size $\pm 10 \rm\ mrad$ were applied to
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the modules/rods of both single and double sided TIB and TOB layers.
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The pixel detector was kept fixed
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in order to define a reference system. In addition, the outermost TOB
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layer was also kept fixed in order to improve the convergence. This
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misalignment scenario was inserted as an alignment object into the
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offline database. In addition, another object corresponding to the
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ideal tracker geometry was inserted, to be used during prompt
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reconstruction.
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\begin{figure}
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\centering
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malgeri |
1.6 |
\includegraphics[angle=270,width=0.8\linewidth]{figs/csa06_convergence}
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fpschill |
1.5 |
\caption{Alignment exercise: The convergence of the alignment
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algorithm in $\Delta u, \Delta v, \Delta w$ is shown for double sided
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TIB modules as a function of the iteration number (left), as well as
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projected for initial misalignment (labeled ``after 0 Iterations'')
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and after 4, 7 and 10 iterations (right). }
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\label{fig:alignment_convergence}
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\end{figure}
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The alignment was performed running the HIP alignment
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algorithm~\cite{hipnote} as implemented in CMSSW\_1\_0\_6 over approx.
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$1 \rm\ M$ AlCaReco $Z^0\rightarrow\mu^+\mu^-$ events produced during
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prompt reconstruction at the Tier-0, reading the above mentioned
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misalignment object from the offline database. The algorithm was run
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on 20 dedicated CPUs in parallel at CERN, iterating 10 times over the
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data sample. The result of the alignment was obtained in less than $5
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\rm\ h$, and the corresponding tracker alignment object
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was inserted into the database. Figure~\ref{fig:alignment_convergence}
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illustrates the convergence of the alignment for the double sided TIB
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sensors.
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\begin{figure}
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\centering
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malgeri |
1.6 |
\includegraphics[angle=270,width=0.8\linewidth]{figs/csa06_aliexercise}
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fpschill |
1.5 |
\caption{Invariant mass distribution from $Z^0\to \mu^+ \mu^-$ events,
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obtained from events produced by the prompt reconstruction at
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the Tier-0 (``ideal''), from events processed with misalignment
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as used as input for the alignment algorithm (``misaligned'') and
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from events re-reconstructed at a Tier-1 centre (PIC) using the alignment
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constants derived from the alignment algorithm (``realigned'').
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}
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\label{fig:aliexercise}
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\end{figure}
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Once the alignment and calibration constants were inserted in the
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database, they were deployed to the Tier-1/2 centres via Frontier.
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Subsequently, re-reconstruction of some of the CSA06 datasets was
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launched at various Tier-1 centres. For instance, the
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$Z^0\rightarrow\mu^+\mu^-$ data set was re-reconstructed at PIC
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(Barcelona) using the new alignment object. In order to demonstrate
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the final missing piece of the workflow, grid analysis jobs were
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submitted to PIC to process these re-reconstructed
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$Z^0\rightarrow\mu^+\mu^-$ events. The reconstructed invariant di-muon
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mass is presented in Figure~\ref{fig:aliexercise} for three cases:
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\begin{itemize}
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\item Using the ideal geometry, reading the RECO produced during prompt
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reconstruction;
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\item Using the misaligned geometry, reading the AlCaReco and the misalignment
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scenario database object;
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\item Using the realigned geometry, reading the events re-reconstructed
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with the alignment database object.
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\end{itemize}
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As can be seen, the invariant mass resolution is degraded in the case
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of misalignment. After the alignment algorithm has corrected the
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tracker geometry, the resolution is recovered close to the original
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value. This demonstrates that the work- and dataflow of the full
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alignment was successfully carried out.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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fisk |
1.1 |
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\subsection {Physics Analysis Exercises}
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ndefilip |
1.2 |
\subsubsection {Effect of tracker misalignment on track reconstruction performances}
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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.
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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:
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\begin{itemize}
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\item the ideal scenario with a perfect tracker geometry;
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\item the short term misalignment scenario supposed to reproduce the mis-alignment conditions during the first
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data taking when the uncertainties on the position of the sub-structures of the CMS
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tracker will be between $10 \, \mu$ for pixel detectors and $400 \, \mu$ for microstrip silicon detectors in
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the endcaps. Detector position and errors are read
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from the offline database at CERN by caching the needed information locally via frontier/squid
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ndefilip |
1.7 |
software.
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ndefilip |
1.3 |
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\item the long term scenario when the alignment uncertainties are supposed to be a factor 10 smaller because of the
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improvement obtained by using aligmnent algorithms with a high statistics of tracks.
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\item the CSA06 aligned scenario by using the tracker module position and errors as obtained by the output of the
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alignment procedure that was run at CERN Tier-0 to verify the efficiency of the alignment procedure on the track
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reconstruction. The refit of tracks is performed also in this case.
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\end{itemize}
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ndefilip |
1.4 |
Track reconstruction is based on the Kalman Filter formalism \cite{Kalman} for trajectory building, cleaning and
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smoothing steps and uses hits from pixel detector as seeds to provide initial trajectory candidates.
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Because of the misalignment the analysis requires to refit tracks with a misaligned tracker geometry.
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Global efficiency of track recostruction and track parameter resolutions for muons were
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compared in all the cases. The association between simulated track and reconstruted tracks is performed
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by comparing the corresponding track parameters at the closest approach point and choosing the pair which gives
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the minimum $\chi^2$ from the best fit procedure.
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ndefilip |
1.3 |
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ndefilip |
1.4 |
Events from CSA06 $Z\rightarrow \mu \mu $ sample were firstly skimmed by selecting events with
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Hep MC muons from Z decay with pseudorapidity, $\eta$, in the tracker acceptance, $|\eta| < 2.55$,
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with transverse momentum larger than $5 \, \mathrm{GeV}/c^2$ and di-muons invariant mass in the following
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range aroung the Z peak: $50 < m_{\mu\mu}(\mathrm{GeV}/c^2) < 130$; the efficiency of the previous selection is
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between 50 and 60 \% mainly due to the cut on the acceptance, for a final statistics of 1 million events.
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The output files in RECOSIM format were needed for the subsequent analysis.
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ndefilip |
1.3 |
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ndefilip |
1.4 |
Jobs executing the misalignment analysis were submitted at Bari with CRAB\_1\_4\_0 in the LCG infrastructure.
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A total of about 2.5 thousands jobs (45 at most in parallel) ran with a grid efficiency of 90 \% and an
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application efficiency of 80\%, by accessing detector position and errors from the offline database via
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frontier.
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ndefilip |
1.3 |
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ndefilip |
1.4 |
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Some results of the misalignment analysis were summarized below. The global efficiency of track reconstruction of muons coming from
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Z decay is shown in Fig.~\ref{eff} as a function of the pseudorapidity, $\eta$, in the tracker acceptance.
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In the case of a perfect geometry the global track reconstruction was not fully efficient over all the $\eta$
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ndefilip |
1.12 |
range because of the track associator algorithm itself which discards tracks with $\chi^2$ of the fit larger
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ndefilip |
1.4 |
than 25. The effect of misalignment is relevant in the short term scenario and causes a partial inefficiency of the
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track reconstruction; that can be recovered if the intrinsic position resolution of the tracker
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detector is combined with the alignment uncertainties to make larger the error on the position of the
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reconstructed hit (called alignment position error, APE) so improving the track fit at the
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expence of a larger rate of fake tracks.
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The tranverse momentum resolution as a function of the transverse momentum is reported in Fig.~\ref{respt};
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the degradation of the tranverse momentum resolution at large $p_{T}$ because of the misalignment is in a factor
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between 2 and 3 with respect to the perfect geometry case. At low transverse momentum (less than few $\mathrm{GeV}/c$
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the multiple scattering is the
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most important contribution to the resolution so the effect of misalignment is overwhelmed at all.
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The residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
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tracker geometry and in short-term and long term misalignment scenarios is shown in Fig.~\ref{mz}; the $\sigma$ of
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the Gaussian fit of the residual ditribution can be quoted as the Z mass resolution which is degradated of a factor 2
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because of the tracker misalignment in the short term scenario.
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\begin{2figures}{hbt}
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malgeri |
1.6 |
\resizebox{\linewidth}{!}{\includegraphics{figs/Eff_eta}} &
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\resizebox{\linewidth}{!}{\includegraphics{figs/SigmapT_pT}} \\
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ndefilip |
1.3 |
\caption{Global track reconstrution efficiency vs pseudorapidity for muons coming from Z decay in the case of perfect
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tracker geometry and in short-term and long term misalignment scenarios when the APE is not used.}
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\label{eff} &
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ndefilip |
1.4 |
\caption{$P_{T}$ resolution vs $p_{T}$ in the case of perfect
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tracker geometry and in short-term and long term misalignment scenarios.}
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\label{respt} \\
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\end{2figures}
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\begin{figure}[htb]
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\begin{center}
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malgeri |
1.6 |
\resizebox{0.7\linewidth}{!}{\includegraphics{figs/Residual_mZ_mu}}
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ndefilip |
1.4 |
\end{center}
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ndefilip |
1.3 |
\caption{Residual of Z mass obtained as the invariant mass of muons coming from Z decay in the case of perfect
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tracker geometry and in short-term and long term misalignment scenarios.}
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ndefilip |
1.4 |
\label{mz}
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\end{figure}
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acosta |
1.10 |
% min bias studies
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\input{mbue.tex}
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acosta |
1.8 |
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% tau analyses:
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ndefilip |
1.4 |
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acosta |
1.8 |
\input{z_2tau.tex}
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\input{taumisid.tex}
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\input{tau_validation.tex}
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acosta |
1.9 |
% muon analyses
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\input{wmunu.tex}
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acosta |
1.13 |
\input{dimuon.tex}
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meridian |
1.11 |
% electron analyses
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\input{zee.tex}
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ndefilip |
1.12 |
\input{wmass.tex}
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