+++ /dev/null
-\documentclass{elsart}
-\usepackage{epsfig,amsmath}
-
-\begin{document}
-
-\subsection{Reconstruction Performance}
-
-A comparison of Monte-Carlo (MC) and reconstruction information is necessary to
-check the performance of the reconstruction algorithms. The PWGPP library has been started to make
-MC vs reconstruction comparison, to tune selection criteria (cuts) and to find the best representation
-of the data (observables, correlations between observables, parametrisations, etc.). The main goal is to
-get the best track reconstruction performance in the central barrel.
-
-\subsection{PWGPP library}
-
-Current implementation of PWGPP allows us to run comparison for TPC and TPC-tracks.
-The comparison of MC and reconstructed tracks is done in 4 steps:
-
-\begin{itemize}
-\item[1.] Collection of MC information
-\item[2.] Correlation of MC and reconstruction information
-\item[3.] Filling of comparison objects
-\item[4.] Analysis of comparison objects
-\end{itemize}
-
-In order to perform these steps the following
-components (classes) have been implemented in PWGPP library:
-
-\begin{itemize}
-\item The $AliGenInfoMaker$ collects MC information stored in
- several simulation trees. It creates $AliMCInfo$, $AliMCKinkInfo$ and $AliMCV0Info$ objects for each MC track.
-\item The $AliRecInfoMaker$ correlates MC and reconstruction information (ESDs) and creates for each MC object (e.g. $AliMCInfo$) corresponding reconstruction object ($AliESDRecInfo$).
-\item In the third step, the comparison objects ($AliComparisonObject$) are filled in $AliComparisonTask$ task. In this step the cuts ($AliMCInfoCuts$, $AliRecInfoCuts$) are applied. The comparison objects keep control histograms and cuts.
-\item Finally, the analysis of the comparison objects is done by using their own $Analyse()$
- functions. The result analyzed histograms can be saved for further studies.
-\end{itemize}
-
-The advantage of such analysis scheme is that the most time consuming
-tasks (steps 1 and 2) can be run only once. It can be done during
-ESD production. Then the comparison task can be run many times (e.g. on Proof)
-to tune selection criteria and to find the best representation of data.
-
-The basic components of the PWGPP library are: makers ($AliGenInfoMaker$, $AliRecInfoMaker$),
-tasks ($AliAnalysisTask$), cuts ($AliAnalysisCuts$) and comparison objects ($AliComparisonObject$).
-
-The makers derive from TObject ROOT class and have functionality to collect and correlate MC and reconstruction
-information for ITS, TPC, TRD and TOF detectors. They can be run locally/batch. The comparison tasks derive from $AliAnalysisTask$ and must implement its virtual methods. They can be run locally/batch, on Proof and on Grid. The cut objects derive from $AliAnalysisCut$ and must implement its virtual methods. The comparison objects derive from $AliComparisonObject$ base class and must implement its virtual methods.
-
-\subsection{User implementation}
-
-One has to implement the following classes to run its own analysis using PWGPP library:
-
-\begin{itemize}
-\item[1.] Implement comparison object which contains control histograms and its own cut object (ex: $PWGPP/AliComparisonDCA.h$)
-\item[2.] Implement (if needed) cut object which contains all cuts which will be applied while filling comparison object. It is recommended to use $IsSelected(TObject*)$ method of the cut object while applying the cuts (ex: $PWGPP/AliMCInfoCuts.h$).
-\end{itemize}
-
-\subsection{Quick Start}
-
-\begin{itemize}
-\item[1.] Prepare input by running $AliGenInfoMaker$ and then $AliRecInfoMaker$ makers ($PWGPP/Macros/RunMakers.C$)
-\item[2.] Create comparison objects
-\item[3.] Create cut objects and pass them to corresponding comparison objects
-\item[4.] Add comparison objects to comparison task \newline ($PWGPP/Macros/RunAliComparisonTask.C$)
-\item[5.] Run comparison analysis ($PWGPP/Macros/RunGSI.C$)
-
-\end{itemize}
-
-\end{document}
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff