source: trunk/MagicSoft/TDAS-Extractor/Algorithms.tex@ 5736

\section{Signal Reconstruction Algorithms \label{sec:algorithms}}

{\it Missing coding: 
\begin{itemize}
\item Real fit to the expected pulse shape \ldots Hendrik, Wolfgang ???
\end{itemize}
}

\subsection{Implementation of Signal Extractors in MARS}

All signal extractor classes are stored in the MARS-directory {\textit{\bf msignal/}}. 
There, the base classes {\textit{\bf MExtractor}}, {\textit{\bf MExtractTime}}, {\textit{\bf MExtractTimeAndCharge}} and 
all individual extractors can be found. Figure~\ref{fig:extractorclasses} gives a sketch of the 
inheritances of each class and what each class calculates.

\begin{figure}[htp]
\includegraphics[width=0.99\linewidth]{ExtractorClasses.eps}
\caption{Sketch of possible inheritances of the MARS signal extractor classes}
\label{fig:extractorclasses}
\end{figure}

The following base classes for the extractor tasks are used:
\begin{description}
\item[MExtractor:\xspace] This class provides the basic data members equal for all extractors which are:
  \begin{enumerate}
  \item Global extraction ranges, parameterized by the variables 
    {\textit{\bf fHiGainFirst, fHiGainLast, fLoGainFirst, fLoGainLast}} and the function {\textit{\bf SetRange()}}. 
    The ranges always {\textit{\bf include}} the edge slices. 
  \item An internal variable {\textit{\bf fHiLoLast}} regulating the overlap of the desired high-gain
    extraction range into the low-gain array.a
  \item The maximum possible FADC value, before the slice is declared as saturated, parameterized 
    by the variable {\textit{\bf fSaturationLimit}} (default:\,254). 
  \item The typical delay between high-gain and low-gain slices, expressed in FADC slices and parameterized
    by the variable {\textit{\bf fOffsetLoGain}} (default:\,1.51)
  \item Pointers to the used storage containers {\textit{\bf MRawEvtData, MRawRunHeader, MPedestalCam}} 
    and~{\textit{\bf MExtractedSignalCam}}, parameterized by the variables 
    {\textit{\bf fRawEvt, fRunHeader, fPedestals}} and~{\textit{\bf fSignals}}.
  \item Names of the used storage containers to be searched for in the parameter list, parameterized 
    by the variables {\textit{\bf fNamePedestalCam}} and~{\textit{\bf fNameSignalCam}} (default: ``MPedestalCam'' 
    and~''MExtractedSignalCam'').
  \item The equivalent number of FADC samples, used for the calculation of the pedestal RMS and then the 
    number of photo-electrons with the F-Factor method (see eq.~\ref{eq:rmssubtraction} and 
    section~\ref{sec:photo-electrons}). This number is parameterized by the variables 
    {\textit{\bf fNumHiGainSamples}} and~{\textit{\bf fNumLoGainSamples}}.
  \end{enumerate}

  {\textit {\bf MExtractor}} is able to loop over all events, if the {\textit{\bf Process()}}-function is not overwritten. 
  It uses the following (virtual) functions, to be overwritten by the derived extractor class:

  \begin{enumerate}
  \item void {\textit {\bf FindSignalHiGain}}(Byte\_t* firstused, Byte\_t* lowgain, Float\_t\& sum, Byte\_t\& sat) const
  \item void {\textit {\bf FindSignalLoGain}}(Byte\_t* firstused, Float\_t\& sum, Byte\_t\& sat) const
  \end{enumerate}

  where the pointers ``firstused'' point to the first used FADC slice declared by the extraction ranges,
  the pointer ``lowgain'' point to the beginning of the low-gain FADC slices array (to be used for 
  pulses reaching into the low-gain array) and the variables ``sum'' and ``sat'' get filled with the 
  extracted signal and the number of saturating FADC slices, respectively.
  \par
  The pedestals get subtracted automatically {\textit {\bf after}} execution of these two functions.

\item[MExtractTime:\xspace] This class provides - additionally to those already declared in {\textit{\bf MExtractor}}, 
  the basic data members equal for all time extractors which are:
  \begin{enumerate}
  \item Pointers to the used storage containers {\textit{\bf MArrivalTimeCam}}
    parameterized by the variables 
    {\textit{\bf fArrTime}}.
  \item The name of the used ``MArrivalTimeCam''-container to be searched for in the parameter list, 
    parameterized by the variables {\textit{\bf fNameTimeCam}} (default: ``MArrivalTimeCam'' ).
  \end{enumerate}

  {\textit {\bf MExtractTime}} is able to loop over all events, if the {\textit{\bf Process()}}-function is not 
  overwritten. 
  It uses the following (virtual) functions, to be overwritten by the derived extractor class:

  \begin{enumerate}
  \item void {\textit {\bf FindTimeHiGain}}(Byte\_t* firstused, Float\_t\& time, Float\_t\& dtime, Byte\_t\& sat, const MPedestlPix \&ped) const
  \item void {\textit {\bf FindTimeLoGain}}(Byte\_t* firstused, Float\_t\& time, Float\_t\& dtime, Byte\_t\& sat, const MPedestalPix \&ped) const
  \end{enumerate}

  where the pointers ``firstused'' point to the first used FADC slice declared by the extraction ranges,
  and the variables ``time'', ``dtime'' and ``sat'' get filled with the 
  extracted arrival time, its error and the number of saturating FADC slices, respectively.
  \par
  The pedestals can be used for the arrival time extraction via the reference ``ped''.

\item[MExtractTimeAndCharge:\xspace] This class provides - additionally to those already declared in 
  {\textit{\bf MExtractor}} and {\textit{\bf MExtractTime}}, 
  the basic data members equal for all time and charge extractors which are:
  \begin{enumerate}
  \item The actual extraction window sizes, parameterized by the variables 
    {\textit{\bf fWindowSizeHiGain}} and {\textit{\bf fWindowSizeLoGain}}.
  \item The shift of the low-gain extraction range start w.r.t. to the found high-gain arrival 
    time, parameterized by the variable {\textit{\bf fLoGainStartShift}} (default: -2.8)
  \end{enumerate}

  {\textit {\bf MExtractTimeAndCharge}} is able to loop over all events, if the {\textit{\bf Process()}}-function is not 
  overwritten. 
  It uses the following (virtual) functions, to be overwritten by the derived extractor class:

  \begin{enumerate}
  \item void {\textit {\bf FindTimeAndChargeHiGain}}(Byte\_t* firstused, Byte\_t* logain, Float\_t\& sum, Float\_t\& dsum, 
    Float\_t\& time, Float\_t\& dtime, Byte\_t\& sat, 
    const MPedestlPix \&ped, const Bool\_t abflag) const
  \item void {\textit {\bf FindTimeAndChargeLoGain}}(Byte\_t* firstused, Float\_t\& sum, Float\_t\& dsum, 
    Float\_t\& time, Float\_t\& dtime, Byte\_t\& sat, 
    const MPedestalPix \&ped, const Bool\_t abflag) const
  \end{enumerate}

  where the pointers ``firstused'' point to the first used FADC slice declared by the extraction ranges,
  the pointer ``lowgain'' point to the beginning of the low-gain FADC slices array (to be used for 
  pulses reaching into the low-gain array),
  the variables ``sum'', ``dsum'' get filled with the 
  extracted signal and its error. The variables ``time'', ``dtime'' and ``sat'' get filled with the 
  extracted arrival time, its error and the number of saturating FADC slices, respectively. 
  \par
  The pedestals can be used for the extraction via the reference ``ped'', also the AB-flag is given 
  for AB-clock noise correction.
\end{description}
  
  
\subsection{Pure Signal Extractors}

The pure signal extractors have in common that they reconstruct only the 
charge, but not  the arrival time. All treated extractors here derive from the MARS-base
class {\textit{\bf MExtractor}} which provides the following facilities:

\begin{itemize}
\item The global extraction limits can be set from outside
\item FADC saturation is kept track of
\end{itemize}

The following freely adjustable parameters have to be set from outside: 
\begin{description}
\item[Global extraction limits:\xspace] Limits in between the extractor is allowed 
to search, independently for high gain and low gain. 
\end{description}

\subsubsection{Fixed Window}

This extractor is implemented in the MARS-class {\textit{MExtractFixedWindow}}.
It simply adds the FADC contents in the assigned ranges. 
As it does not correct for the clock-noise, only an even number of samples is allowed.

\subsubsection{Fixed Window with Integrated Cubic Spline}

This extractor is implemented in the MARS-class {\textit{MExtractFixedWindowSpline}},
using a cubic spline algorithm, adapted from \cite{NUMREC}. 
It integrated the 
spline interpolated FADC slice values. The edge slices are counted as half.
As it does not correct for the clock-noise, only an odd number of samples is allowed.

\subsubsection{Fixed Window with Global Peak Search}

This extractor is implemented in the MARS-class {\textit{MExtractFixedWindowPeakSearch}}.
It fixes first a reference point defined as the highest sum of 
consecutive non-saturating FADC slices in a (smaller) peak-search window. This reference 
point removes the coherent movement of the arrival times in the whole camera due to the trigger jitter.
\par
In a second loop over the pixels, 
it adds the FADC contents starting from a pre-defined point before the obtained peak-search window 
over an extraction window of a pre-defined window size.
It loops twice over all pixels in every event, because it has to find the reference point, first. 
As it does not correct for the clock-noise, only an even number of samples is allowed.

The following freely adjustable parameters have to be set from outside: 
\begin{description}
\item[Peak Search Window:\xspace] Defines the ``sliding window'' in which the peaking sum is 
searched for (default: 4 slices)
\item[Offset from Window:\xspace] Defines the offset of the start of the extraction window w.r.t. the 
starting point of the obtained peak search window (default: 1 slice)
\item[Low-Gain Peak shift:\xspace] Defines the shift in the low-gain with respect to the peak found 
in the high-gain (default: 1 slice)
\end{description}

\subsection{Combined Extractors}

The combined extractors have in common that they reconstruct the arrival time and 
the charge at the same time and for the same pulse. 
All treated combined extractors here derive from the MARS-base
class {\textit{MExtractTimeAndCharge}} which provides the following facilities:

\begin{itemize}
\item Only one loop over all pixels is performed
\item The individual FADC slice values get the clock-noise-corrected pedestals immediately subtracted.
\item The low-gain extraction range is adapted dynamically, based on the computed arrival time 
    from the high-gain samples
\item Extracted times from the low-gain samples get corrected for the intrinsic time delay of the low-gain
    pulse
\item The global extraction limits can be set from outside
\item FADC saturation is kept track of
\end{itemize}

The following free adjustable parameters have to be set additionally from outside: 
\begin{description}
\item[Global extraction limits:\xspace] Limits in between which the extractor is allowed 
to search. They are fixed by the extractor for the high-gain, but re-adjusted for 
every event in the low-gain, depending on the arrival time found in the low-gain. 
However, the dynamically adjusted window is not allowed to pass beyond the global 
limits.
\item[Low-gain start shift:\xspace] Global shift between the computed high-gain arrival
time and the start of the low-gain extraction limit (corrected for the intrinsic time offset). 
This variable tells where the extractor is allowed to start searching for the low-gain signal 
if the high-gain arrival time is known. It avoids that the extractor gets confused by possible high-gain 
signals leaking into the ``low-gain'' region (default: -2.8).
\end{description}

\subsubsection{Sliding Window with Amplitude-Weighted Time}

This extractor is implemented in the MARS-class {\textit{MExtractTimeAndChargeSlidingWindow}}.
It extracts the signal from a sliding window of an adjustable size, for high-gain and low-gain 
individually (default: 6 and 6) The signal is the one which maximizes the summed 
(clock-noise and pedestal-corrected) FADC slice contents.
\par
The amplitude-weighted arrival time is calculated from the window with 
the highest integral using the following formula:

\begin{equation}
  t = \frac{\sum_{i=0}^{windowsize} s_i \cdot i}{\sum_{i=0}^{windowsize} i} 
\end{equation}
where $i$ denotes the FADC slice index, starting from the beginning of the derived 
window and running over the window and $s_i$ the clock-noise and 
pedestal-corrected FADC value at slice index i. 
\par
The following free adjustable parameters have to be set from outside: 
\begin{description}
\item[Window sizes:\xspace] Independently for high-gain and low-gain (default: 6,6)
\end{description}

\subsubsection{Cubic Spline with Sliding Window or Amplitude Extraction}

This extractor is implemented in the MARS-class {\textit{MExtractTimeAndChargeSpline}}.
It uses a cubic spline algorithm, adapted from \cite{NUMREC}. 
The following free adjustable parameters have to be set from outside: 

\begin{description}
\item[Time Extraction Type:\xspace] The position of the maximum can be chosen (default) or the 
position of the half maximum at the rising edge of the pulse
\item[Charge Extraction Type:\xspace] The amplitude of the maximum can be chosen (default) or the 
integrated spline between maximum position minus rise time (default: 1.5 slices) and maximum position plus
fall time (default: 4.5 slices). The low-gain signal integrates one slice more at the falling part of the 
signal.
\item[Rise Time and Fall Time:\xspace] Can be adjusted for the integration charge extraction type.
\item[Resolution:\xspace] Defined as the maximum allowed difference between the calculated half maximum value and 
the computed spline value at the arrival time position. Can be adjusted for the half-maximum time extraction 
type.
\end{description}

\subsubsection{Digital Filter}

This extractor is implemented in the MARS-class {\textit{MExtractTimeAndChargeDigitalFilter}}.


The goal of the digital filtering method \cite{OF94,OF77} is to optimally reconstruct the amplitude and time origin of a signal with a known signal shape from discrete measurements of the signal. Thereby the noise contribution to the amplitude reconstruction is minimized.

For the digital filtering method two assumptions have to be made:

\begin{itemize}
\item{The normalized signal shape has to be independent of the signal amplitude.}
\item{The noise properties have to be independent of the signal amplitude.}
\end{itemize}

Let $g(t)$ be the normalized signal shape, $E$ the signal amplitude and $\tau$ the time shift of the physical signal from the predicted signal shape. Then the time dependence of the signal, $y(t)$, is given by:

\begin{equation}
y(t)=E \cdot  g(t-\tau) + b(t) \ ,
\end{equation}

where $b(t)$ is the time dependent noise distribution. For small time shifts $\tau$ the time dependence can be linearized by the use of a Taylor expansion:

\begin{equation} \label{shape_taylor_approx}
y(t)=E \cdot  g(t) - E\tau \cdot  \dot{g}(t) + b(t) \ ,
\end{equation}

where $\dot{g}(t)$ is the time derivative of the signal shape. Discrete
measurements $y_i$ of the signal at times $t_i \ (i=1,...,n)$ have the form:

\begin{equation}
y_i=E \cdot g_i- E\tau \cdot \dot{g}_i +b_i \ .
\end{equation}

The correlation of the noise contributions at times $t_i$ and $t_j$ is expressed in the noise autocorrelation matrix $\boldsymbol{B}$:

\begin{equation} 
\boldsymbol{B_{ij}} = \langle b_i b_j \rangle - \langle b_i \rangle \langle b_j
\rangle  \ .
\label{eq:autocorr}
\end{equation}
%\equiv \langle b_i b_j \rangle with $\langle b_i \rangle = 0$.

The signal amplitude, $E$, and the product of amplitude and time shift, $E \tau$, can be estimated from the given set of
measurements $\boldsymbol{y} = (y_1, ... ,y_n)$ by minimizing:

\begin{eqnarray}
\chi^2(E, E\tau) &=& \sum_{i,j}(y_i-E g_i-E\tau \dot{g}_i) \boldsymbol{B}^{-1}_{ij} (y_j - E g_j-E\tau \dot{g}_j) \\
&=& (\boldsymbol{y} - E
\boldsymbol{g} - E\tau \dot{\boldsymbol{g}})^T \boldsymbol{B}^{-1} (\boldsymbol{y} - E \boldsymbol{g}- E\tau \dot{\boldsymbol{g}}) \ ,
\end{eqnarray}

where the last expression is matricial. The minimum is obtained for:

\begin{equation}
\frac{\partial \chi^2(E, E\tau)}{\partial E} = 0 \qquad \text{and} \qquad \frac{\partial \chi^2(E, E\tau)}{\partial(E\tau)} = 0 \ .
\end{equation}

Taking into account that $\boldsymbol{B}$ is a symmetric matrix, this leads to the following two equations for the estimated amplitude $\overline{E}$ and the estimation for the product of amplitude and time offset $\overline{E\tau}$:

\begin{eqnarray}
0&=&-\boldsymbol{g}^T\boldsymbol{B}^{-1}\boldsymbol{y}+\boldsymbol{g}^T\boldsymbol{B}^{-1}\boldsymbol{g}\overline{E}+\boldsymbol{g}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}\overline{E\tau} 
\\
0&=&-\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{y}+\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g}\overline{E}+\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}\overline{E\tau} \ .
\end{eqnarray}

Solving these equations one gets the solutions:

\begin{equation}
\overline{E}= \boldsymbol{w}_{\text{amp}}^T \boldsymbol{y} \quad \mathrm{with} \quad \boldsymbol{w}_{\text{amp}} = \frac{ (\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) \boldsymbol{B}^{-1} \boldsymbol{g} -(\boldsymbol{g}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}})  \boldsymbol{B}^{-1} \dot{\boldsymbol{g}}}  {(\boldsymbol{g}^T \boldsymbol{B}^{-1} \boldsymbol{g})(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) -(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g})^2 } \ ,
\end{equation}

\begin{equation}
\overline{E\tau}= \boldsymbol{w}_{\text{time}}^T \boldsymbol{y} \quad \mathrm{with} \quad \boldsymbol{w}_{\text{time}} = \frac{ ({\boldsymbol{g}}^T\boldsymbol{B}^{-1}{\boldsymbol{g}}) \boldsymbol{B}^{-1} \dot{\boldsymbol{g}} -(\boldsymbol{g}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}})  \boldsymbol{B}^{-1} {\boldsymbol{g}}}  {(\boldsymbol{g}^T \boldsymbol{B}^{-1} \boldsymbol{g})(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) -(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g})^2 } \ .
\end{equation}


Thus $\overline{E}$ and $\overline{E\tau}$ are given by a weighted sum of the discrete measurements $y_i$ with the digital filtering weights for the amplitude, $w_{\text{amp},i}$, and time shift, $w_{\text{time},i}$.

Because of the truncation of the Taylor series in equation (\ref{shape_taylor_approx}) the above results are only valid for vanishing time offsets $\tau$. For non-zero time offsets one has to iterate the problem using the time shifted signal shape $g(t-\tau)$.

The covariance matrix $\boldsymbol{V}$ of $\overline{E}$ and $\overline{E\tau}$ is given by:

\begin{equation}
\left(\boldsymbol{V}^{-1}\right)_{i,j}=\frac{1}{2}\left(\frac{\partial^2 \chi^2(E, E\tau)}{\partial \alpha_i \partial \alpha_j} \right) \quad \text{with} \quad \alpha_i,\alpha_j \in {E, E\tau} \ .
\end{equation}

The expected contribution of the noise to the estimated amplitude, $\sigma_E$, is:

\begin{equation}\label{of_noise}
\sigma_E^2=\boldsymbol{V}_{E,E}=\frac{\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}}{(\boldsymbol{g}^T \boldsymbol{B}^{-1} \boldsymbol{g})(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) -(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g})^2} \ .
\end{equation}

The expected contribution of the noise to the estimated timing, $\sigma_{\tau}$, is:

\begin{equation}\label{of_noise_time}
E^2 \cdot \sigma_{\tau}^2=\boldsymbol{V}_{E,E\tau}=\frac{{\boldsymbol{g}}^T\boldsymbol{B}^{-1}{\boldsymbol{g}}}{(\boldsymbol{g}^T \boldsymbol{B}^{-1} \boldsymbol{g})(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) -(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g})^2} \ .
\end{equation}

For the MAGIC signals, as implemented in the MC simulations, a pedestal RMS of a single FADC slice of 4 FADC counts introduces an error in the reconstructed signal and time of:

\begin{equation}\label{of_noise}
\sigma_E \approx 8.3 \ \mathrm{FADC\ counts} \qquad \sigma_{\tau}  \approx  \frac{6.5\ \Delta T_{\mathrm{FADC}}}{E\ /\ \mathrm{FADC\ counts}} \ ,
\end{equation}

where $\Delta T_{\mathrm{FADC}} = 3.33$ ns is the sampling interval of the MAGIC FADCs.


For an IACT there are two types of background noise. On the one hand there is the constantly present electronics noise, on the other hand the light of the night sky introduces a sizeable background noise to the measurement of Cherenkov photons from air showers.

The electronics noise is largely white, uncorrelated in time. The noise from the night sky background photons is the superposition of the detector response to single photo electrons arriving randomly distributed in time. Figure \ref{fig:noise_autocorr_AB_36038_TDAS} shows the noise autocorrelation matrix for an open camera. The large noise autocorrelation in time of the current FADC system is due to the pulse shaping with a shaping constant of 6 ns. 

In general the amplitude and timing weights, $\boldsymbol{w}_{\text{amp}}$ and $\boldsymbol{w}_{\text{time}}$, depend on the pulse shape, the derivative of the pulse shape and the noise autocorrelation. In the high gain samples the correlated night sky background noise dominates over the white electronics noise. Thus different noise levels just cause the noise autocorrelation matrix $\boldsymbol{B}$ to change by a factor, which cancels out in the weights calculation. Thus in the high gain the weights are to a very good approximation independent of the night sky background noise level.

Contrary to that in the low gain samples ... .



\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{noise_autocorr_AB_36038_TDAS.eps}
\end{center}
\caption[Noise autocorrelation.]{Noise autocorrelation matrix for open camera including the noise due to night sky background fluctuations.} \label{fig:noise_autocorr_AB_36038_TDAS}
\end{figure}



Using the average reconstructed pulpo pulse shape, see figure \ref{fig:pulpo_shape_low}, and the reconstructed noise autocorrelation matrix from a pedestal run with random triggers, the digital filter weights are computed. Figures \ref{fig:w_time_MC_input_TDAS} and \ref{fig:w_amp_MC_input_TDAS} show the parameterization of the amplitude and timing weights for the MC pulse shape as a function of the ...


\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{w_time_MC_input_TDAS.eps}
\end{center}
\caption[Time weights.]{Time weights $w_{\mathrm{time}}(t_0) \ldots w_{\mathrm{time}}(t_5)$ for a window size of 6 FADC slices for the pulse shape used in the MC simulations. The first weight $w_{\mathrm{time}}(t_0)$ is plotted as a function of the relative time $t_{\text{rel}}$ the trigger and the FADC clock in the range $[-0.5;0.5[ \ T_{\text{ADC}}$, the second weight in the range $[0.5;1.5[ \ T_{\text{ADC}}$ and so on. A binning resolution of $0.1  T_{\text{ADC}}$ has been chosen.} \label{fig:w_time_MC_input_TDAS}
\end{figure}

\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{w_amp_MC_input_TDAS.eps}
\end{center}
\caption[Amplitude weights.]{Amplitude weights $w_{\mathrm{amp}}(t_0) \ldots w_{\mathrm{amp}}(t_5)$ for a window size of 6 FADC slices for the pulse shape used in the MC simulations. The first weight $w_{\mathrm{amp}}(t_0)$ is plotted as a function of the relative time $t_{\text{rel}}$ the trigger and the FADC clock in the range $[-0.5;0.5[ \ T_{\text{ADC}}$, the second weight in the range $[0.5;1.5[ \ T_{\text{ADC}}$ and so on. A binning resolution of $0.1  T_{\text{ADC}}$ has been chosen.} \label{fig:w_amp_MC_input_TDAS}
\end{figure}



In the current implementation a two step procedure is applied to reconstruct the signal. The weight functions $w_{\mathrm{amp}}(t)$ and $w_{\mathrm{time}}(t)$ are computed numerically with a resolution of $1/10$ of an FADC slice. In the first step the quantities $e_{i_0}$ and $e\tau_{i_0}$ are computed using a window of $n$ slices:

\begin{equation}
e_{i_0}=\sum_{i=i_0}^{i=i_0+n-1} w_{\mathrm{amp}}(t_i)y(t_{i+i_0}) \qquad (e\tau)_{i_0}=\sum_{i=i_0}^{i=i_0+n-1} w_{\mathrm{time}}(t_i)y(t_{i+i_0}) 
\end{equation}

for all possible signal start slices $i_0$. Let $i_0^*$ be the signal start slice with the largest $e_{i_0}$. Then in a second step the timing offset $\tau$ is calculated:

\begin{equation}
\tau=\frac{(e\tau)_{i_0^*}}{e_{i_0^*}}
\end{equation}

and the weights iterated:

\begin{equation}
E=\sum_{i=i_0^*}^{i=i_0^*+n-1} w_{\mathrm{amp}}(t_i - \tau)y(t_{i+i_0^*}) \qquad E \theta=\sum_{i=i_0^*}^{i=i_0^*+n-1} w_{\mathrm{time}}(t_i - \tau)y(t_{i+i_0^*}) \ .
\end{equation}

The reconstructed signal is then taken to be $E$ and the reconstructed arrival time $t_{\text{arrival}}$ is 

\begin{equation}
t_{\text{arrival}} = i_0^* + \tau + \theta \ .
\end{equation}



% This does not apply for MAGIC as the LONs are giving always a correlated noise (in addition to the artificial shaping)

%In the case of an uncorrelated noise with zero mean the noise autocorrelation matrix is:

%\begin{equation}
%\boldsymbol{B}_{ij}= \langle b_i b_j \rangle \delta_{ij} = \sigma^2(b_i) \ ,
%\end{equation}

%where $\sigma(b_i)$ is the standard deviation of the noise of the discrete measurements. Equation (\ref{of_noise}) than becomes:


%\begin{equation}
%\frac{\sigma^2(b_i)}{\sigma_E^2} = \sum_{i=1}^{n}{g_i^2} - \frac{\sum_{i=1}^{n}{g_i \dot{g}_i}}{\sum_{i=1}^{n}{\dot{g}_i^2}} \ .
%\end{equation}







\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{amp_sliding.eps}
\includegraphics[totalheight=7cm]{time_sliding.eps}
\end{center}
\caption[Digital filter weights applied.]{Digital filter weights applied to the recorded FADC time slices of one calibration pulse. The left plot shows the result of the applied amplitude weights $e(t_0)=\sum_{i=0}^{i=n-1} w_{\mathrm{amp}}(t_0+i \cdot T_{\text{ADC}})y(t_0+i \cdot T_{\text{ADC}})$ and the right plot shows the result of the applied timing weights $e\tau(t_0)=\sum_{i=0}^{i=n-1} w_{\mathrm{time}}(t_0+i \cdot T_{\text{ADC}})y(t_0+i \cdot T_{\text{ADC}})$ .} \label{fig:amp_sliding}
\end{figure}


Figure \ref{fig:shape_fit_TDAS} shows the FADC slices of a single MC event together with the result of a full fit of the input MC pulse shape to the simulated FADC samples together with the result of the numerical fit using the digital filter. 


\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{shape_fit_TDAS.eps}
\end{center}
\caption[Shape fit.]{Full fit to the MC pulse shape with the MC input shape and a numerical fit using the digital filter.} \label{fig:shape_fit_TDAS}
\end{figure}







\ldots {\it Hendrik ... }

The following free adjustable parameters have to be set from outside: 

\begin{description}
\item[Weights File:\xspace] 
\item[Window Sizes:\xspace]
\item[Binning Resolution:\xspace]
\end{description}

\subsubsection{Real Fit to the Expected Pulse Shape }

This extractor is not yet implemented as MARS-class...
\par
It fit the pulse shape to a Landau convoluted with a Gaussian using the following 
parameters:...

\ldots {\it Hendrik, Wolfgang ... }

\begin{figure}[h!]
\begin{center}
\includegraphics[totalheight=7cm]{probability_fit_0ns.eps}
\end{center}
\caption[Fit Probability.]{Probability of the fit with the input signal shape to the simulated FADC samples including electronics and NSB noise.} \label{fig:w_amp_MC_input_TDAS.eps}
\end{figure}



\subsection{Used Extractors for this Analysis}

We propose to test the following parameterized extractors:

\begin{description}
\item[MExtractFixedWindow]: with the following parameters, if {\textit{maxbin}} defines the 
   mean position of the High-Gain FADC slice carrying the pulse maximum:
\begin{enumerate}
\item SetRange({\textit{maxbin}}-1,{\textit{maxbin}}+2,{\textit{maxbin}}+0.5,{\textit{maxbin}}+3.5);
\item SetRange({\textit{maxbin}}-1,{\textit{maxbin}}+2,{\textit{maxbin}}-0.5,{\textit{maxbin}}+4.5);
\item SetRange({\textit{maxbin}}-2,{\textit{maxbin}}+3,{\textit{maxbin}}-0.5,{\textit{maxbin}}+4.5);
\item SetRange({\textit{maxbin}}-3,{\textit{maxbin}}+4,{\textit{maxbin}}-1.5,{\textit{maxbin}}+5.5);
\item SetRange({\textit{maxbin}}-5,{\textit{maxbin}}+8,{\textit{maxbin}}-1.5,{\textit{maxbin}}+7.5);
\suspend{enumerate}
\item[MExtractFixedWindowSpline]: with the following parameters, if {\textit{maxbin}} defines the 
   mean position of the High-Gain FADC slice carrying the pulse maximum:
\resume{enumerate}
\item SetRange({\textit{maxbin}}-1,{\textit{maxbin}}+2,{\textit{maxbin}}+0.5,{\textit{maxbin}}+3.5);
\item SetRange({\textit{maxbin}}-1,{\textit{maxbin}}+2,{\textit{maxbin}}-0.5,{\textit{maxbin}}+4.5);
\item SetRange({\textit{maxbin}}-2,{\textit{maxbin}}+3,{\textit{maxbin}}-0.5,{\textit{maxbin}}+4.5);
\item SetRange({\textit{maxbin}}-3,{\textit{maxbin}}+4,{\textit{maxbin}}-1.5,{\textit{maxbin}}+5.5);
\item SetRange({\textit{maxbin}}-5,{\textit{maxbin}}+8,{\textit{maxbin}}-1.5,{\textit{maxbin}}+7.5);
\suspend{enumerate}
\item[MExtractFixedWindowPeakSearch]: with the following parameters: \\
SetRange(0,18,2,14); and:
\resume{enumerate}
\item SetWindows(2,2,2); SetOffsetFromWindow(0);
\item SetWindows(4,4,2);
\item SetWindows(4,6,4); SetOffsetFromWindow(0);
\item SetWindows(6,6,4);
\item SetWindows(8,8,4);
\item SetWindows(14,10,4); SetOffsetFromWindow(2);
\suspend{enumerate}
\item[MExtractTimeAndChargeSlidingWindow]: with the following parameters: \\
SetRange(0,18,2,14); and:
\resume{enumerate}
\item SetWindowSize(2,2);
\item SetWindowSize(4,4);
\item SetWindowSize(4,6);
\item SetWindowSize(6,6);
\item SetWindowSize(8,8);
\item SetWindowSize(14,10);
\suspend{enumerate}
\item[MExtractTimeAndChargeSpline]: with the following parameters:
\resume{enumerate}
\item SetChargeType(MExtractTimeAndChargeSpline::kAmplitude); \\
SetRange(0,10,4,11); 
\suspend{enumerate}
SetChargeType(MExtractTimeAndChargeSpline::kIntegral); \\
SetRange(0,18,2,14); \\
and:
\resume{enumerate}
\item SetRiseTime(0.5); SetFallTime(0.5);
\item SetRiseTime(0.5); SetFallTime(1.5);
\item SetRiseTime(1.0); SetFallTime(3.0);
\item SetRiseTime(1.5); SetFallTime(4.5);
\suspend{enumerate}
\item[MExtractTimeAndChargeDigitalFilter]: with the following parameters:
\resume{enumerate}
\item SetWeightsFile(``cosmic\_weights6.dat'');
\item SetWeightsFile(``cosmic\_weights4.dat'');
\item SetWeightsFile(``cosmic\_weights\_logain6.dat'');
\item SetWeightsFile(``cosmic\_weights\_logain4.dat'');
\item SetWeightsFile(``calibration\_weights\_UV6.dat'');
\suspend{enumerate}
\item[``Real Fit'']: (not yet implemented, one try)
\resume{enumerate}
\item Real Fit
\end{enumerate}
\end{description}

Note that the extractors \#30, \#31 are used only to test the stability of the extraction against 
changes in the pulse-shape.

References: \cite{OF77,OF94}.


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