dsilverm doc additions - mainly regarding raw data

diff --git a/EMCAL/doc/reconstruction.tex b/EMCAL/doc/reconstruction.tex
index a7dedd70cebf83b3733ae262365b84f28d864ccf..20802f06e517cc579ff78cc212f1b1988429bc98 100644 (file)
--- a/EMCAL/doc/reconstruction.tex
+++ b/EMCAL/doc/reconstruction.tex
@@ -28,9 +28,35 @@ How to create explained OCDB/OADB section.
 
 \subsection{Raw data fitting: from ADC sample to digits - David}
 
-\begin{lstlisting}
-AliEMCALRawUtils, AliCaloRawAnalyzer*, AliCalo*, AliEMCALDigit.
-\end{lstlisting}
+As also discussed in Sec.~\ref{sec:simu_raw}, the recorded Raw data consists
+of instead a series of so-called time samples with 10-bit ADC counts per channel. 
+Each time bin is 100 ns wide, corresponding to a 10 MHz readout.
+The expected signal (Gamma-2) shape is described e.g. in the AliEMCALRawResponse class,
+in the RawResponseFunction method.
+The reconstruction from Raw data to Digits is done in the AliEMCALRawUtils class,
+Raw2Digits method.
+The Raw ADC time samples data is kept in AliCaloBunchInfo objects, which are given
+as input to an AliCaloRawAnalyzer object, which returns the signal amplitude and time
+information (in the form of an AliCaloFitResults object).
+There are several different AloCaloRawAnalyzer versions, which can be selected via
+AliEMCALRawUtils::SetFittingAlgorithm(). They are:
+
+\begin{itemize}
+ \item kStandard:
+   AliCaloRawAnalyzerKStandard, which is a (slower but simple) Gamma-2 fit implementation.
+ \item  kFastFit: 
+   AliCaloRawAnalyzerFastFit, which is a faster Gamma-2 fit implementation from Aleksei Pavlinov.
+ \item kNeuralNet:
+   AliCaloRawAnalyzerNN, which is a neural network implementation from Paola La Rocca and Franco Riggi.
+ \item kPeakFinder:
+   AliCaloRawAnalyzerPeakFinder, which is a fast (parameterized vector operations) implementation from Per Thomas Hille. 
+ \item kCrude:
+   AliCaloRawAnalyzerCrude, which is the simplest possible algorithm: just take the maximum ADC value as the signal amplitude.
+ \item kFakeAltro:
+   AliCaloRawAnalyzerFakeALTRO, which is an algorithm intended for the Trigger/TRU raw data analysis, i.e. not for the regular FEE or cell/tower data.
+\end{itemize}
+
+
 
 \subsection{Clusterization: From digits to clusters - Adam}
 

diff --git a/EMCAL/doc/simulation.tex b/EMCAL/doc/simulation.tex
index 880c38a20e1f3ac5ce6e0e77893085db31bc5e7e..1abb4f8f2f8c0cfec61fceda613e6bc2906a76a9 100644 (file)
--- a/EMCAL/doc/simulation.tex
+++ b/EMCAL/doc/simulation.tex
@@ -42,19 +42,25 @@ is added and Digits whose energy does not pass an energy threshold
 \textbf{EMCAL.SDigits.root} and \textbf{EMCAL.Digits.root}, respectively.
 \end{enumerate}
 
-\subsection{Raw data - David}
-
-The experiment does not record Digits directly but a
-time samples of ADC counts per cell. These samples are called
-\textbf{Raw Data}. The samples have a shape, more complicated than
-a Gaussian distribution, which is fitted offline. With real data,
+\subsection{Raw data - David \label{sec:simu_raw}}
+
+The experiment does not record Digits directly, but instead a series of so-called
+time samples with 10-bit ADC counts per channel. Each time bin is 100 ns
+wide, corresponding to a 10 MHz readout.
+These samples are referred to as
+\textbf{Raw Data}. The samples follow a certain signal shape, more complicated than
+a Gaussian distribution, which is fitted offline. 
+The simulated signal (Gamma-2) shape is described in the AliEMCALRawResponse class,
+in the RawResponseFunction method.
+With real data, which is zero-suppressed, i.e. has the pedestal subtracted online, the
 Digits amplitude is just the maximum of the distribution obtained
 with the fit to the sample. The Digit time (defined by the time the
-particle hits the active volume of the detector) is the time bin when
-the signal begins to rise. There is a method to go from Digits to
-Raw and vice versa AliEMCALRawUtils class: Raw2Digits and Digits2Raw,
+particle hits the active volume of the detector) is the time value at
+the maximum signal fit. There are methods to go from Digits to
+Raw and vice versa in the AliEMCALRawUtils class: Raw2Digits and Digits2Raw,
 respectively. For the reconstruction step Digits are needed. The
-generation of Raw Data is optional during simulations and the generated data can be reconstructed directly from Digits, but Raw data will be the initial
+generation of Raw Data is optional during simulations and the generated 
+data can be reconstructed directly from Digits, but Raw data is the initial
 step when reconstructing real data.
 
 

diff --git a/EMCAL/doc/strategy.tex b/EMCAL/doc/strategy.tex
index efd700cade440893e0ec580228e3e295dd33c0d3..69596968adb756ce1293420e67d19c2a6bc99ec4 100644 (file)
--- a/EMCAL/doc/strategy.tex
+++ b/EMCAL/doc/strategy.tex
@@ -108,6 +108,9 @@ Finally, the first iteration needs the recalibration factors. This file is made
 \subsubsection{Energy calibration: Run by run temperature gain variations - Evi, David  }
 
 The SuperModules calibration depends on the temperature dependence of the different towers gains. We observe that from one period to other, where the T changes, the $\pi^{0}$ peak positions also changes. There are 2 ways to correct for this effect : either measure the mean T per run, and get the gain curves per tower a calculate the corresponding correction; or use the calibration LED events to quantify the variation from one reference run. Each of those 2 procedures have problems, poor or lack of knowledge of the gain curves of some towers or bad performance of the LED system in certain regions.
+These temperature or time-dependent corrections are still under study: for further, and up-to-date, information, please see the wiki: 
+https://twiki.cern.ch/twiki/bin/viewauth/ALICE/EMCalTimeDependentCalibrations
+
 
 \subsubsection{Time calibration - Marie }