source: trunk/MagicSoft/TDAS-Extractor/Performance.tex@ 5913

\section{Performance}

\subsection{Calibration}

In this section, we describe the tests performed using light pulses of different colour, 
pulse shapes and intensities with the MAGIC LED Calibration Pulser Box \cite{hardware-manual}. 
\par
The LED pulser system is able to provide fast light pulses of 3--4\,ns FWHM 
with intensities ranging from 3--4 photo-electrons to more than 500 in one inner pixel of the 
camera. These pulses can be produced in three colours $green$, $blue$ and $UV$.

\begin{table}[htp]
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\hline
\multicolumn{7}{|c|}{The possible pulsed light colours} \\
\hline
\hline
Colour &  Wavelength & Spectral Width & Min. Nr. &  Max. Nr. & Secondary & FWHM \\
      & [nm]         & [nm]           &  Phe's   &  Phe's    & Pulses  &  Pulse [ns]\\
\hline
Green &  520      & 40      & 6          &  120      & yes  & 3--4  \\
\hline
Blue &  460       & 30      & 6          &  500      & yes  & 3--4 \\
\hline
UV   &  375       & 12      & 3          &  50       & no   & 2--3 \\ 
\hline
\hline
\end{tabular}
\caption{The pulser colours available from the calibration system}
\label{tab:pulsercolours}
\end{table}

Table~\ref{tab:pulsercolours} lists the available colours and intensities and 
figures~\ref{fig:pulseexample1leduv} and~\ref{fig:pulseexample23ledblue} show exemplary pulses 
as registered by the FADCs.
Whereas the UV-pulse is very stable, the green and blue pulses show sometimes smaller secondary 
pulses after about 10--40\,ns from the main pulse.
One can see that the very stable UV-pulses are unfortunately only available in such intensities as to 
not saturate the high-gain readout channel. However, the brightest combination of light pulses easily 
saturates all channels in the camera, but does not reach a saturation of the low-gain readout.
\par
Our tests can be classified into three subsections:

\begin{enumerate}
\item Un-calibrated pixels and events: These tests measure the percentage of failures of the extractor 
resulting either in a pixel declared as un-calibrated or in an event which produces a signal ouside 
of the expected Gaussian distribution.
\item Number of photo-electrons: These tests measure the reconstructed numbers of photo-electrons, their 
spread over the camera and the ratio of the obtained mean values for outer and inner pixels, respectively.
\item Linearity tests: These tests measure the linearity of the extractor with respect to pulses of 
different intensity and colour.
\item Time resolution: These tests show the time resolution and stability obtained with different 
intensities and colours.
\end{enumerate}

\begin{figure}[htp]
\centering
\includegraphics[width=0.48\linewidth]{1LedUV_Pulse_Inner.eps}
\includegraphics[width=0.48\linewidth]{1LedUV_Pulse_Outer.eps}
\caption{Example of a calibration pulse from the lowest available intensity (1\,Led UV). 
The left plot shows the signal obtained in an inner pixel, the right one the signal in an outer pixel.
Note that the pulse height fluctuates much more than suggested from these pictures. Especially, a 
zero-pulse is also possible.}
\label{fig:pulseexample1leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.48\linewidth]{23LedsBlue_Pulse_Inner.eps}
\includegraphics[width=0.48\linewidth]{23LedsBlue_Pulse_Outer.eps}
\caption{Example of a calibration pulse from the highest available mono-chromatic intensity (23\,Leds Blue). 
The left plot shows the signal obtained in an inner pixel, the right one the signal in an outer pixel.
One the left side of both plots, the (saturated) high-gain channel is visible, 
on the right side from FADC slice 18 on, 
the delayed low-gain 
pulse appears. Note that in the left plot, there is a secondary pulses visible in the tail of the 
high-gain pulse. }
\label{fig:pulseexample23ledblue}
\end{figure}

We used data taken on the 7$^{th}$ of June, 2004 with different pulser LED combinations, each taken with 
16384 events. The corresponding run numbers range from nr. 31741 to 31772. This data was taken before the 
latest camera repair access which resulted in a replacement of about 2\% of the pixels known to be 
mal-functionning at that time.
Thus, there is a lower limit to the number of un-calibrated pixels of about 1.5--2\% known 
mal-functionning pixels.
\par
Although we had looked at and tested all colour and extractor combinations resulting from these data, 
we refrain ourselves to show here only exemplary behaviour and results of extractors. 
All plots, including those which are not displayed in this TDAS, can be retrieved from the following 
locations:

\begin{verbatim}
http://www.magic.ifae.es/~markus/pheplots/
http://www.magic.ifae.es/~markus/timeplots/
\end{verbatim}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsubsection{Un-Calibrated Pixels and Events}

The MAGIC calibration software incorporates a series of checks to sort out mal-functionning pixels. 
Except for the software bug searching criteria, the following exclusion reasons can apply:

\begin{enumerate}
\item The reconstructed mean signal is less than 2.5 times the extractor resolution $R$ from zero. 
(2.5 Pedestal RMS in the case of the simple fixed window extractors, see section~\ref{sec:pedestals}). 
This criterium essentially cuts out 
dead pixels.
\item The reconstructed mean signal error is smaller than its value. This criterium cuts out 
signal distributions which fluctuate so much that their RMS is bigger than its mean value. This 
criterium cuts out ``ringing'' pixels or mal-functionning extractors. 
\item The reconstructed mean number of photo-electrons lies 4.5 sigma outside 
the distribution of photo-electrons obtained with the inner or outer pixels in the camera, respectively. 
This criterium cuts out pixels channels with apparently deviating (hardware) behaviour compared to 
the rest of the camera readout.
\item All pixels with reconstructed negative mean signal or with a 
mean numbers of photo-electrons smaller than one. Pixels with a negative pedestal RMS subtracted 
sigma occur, especially when stars are focussed onto that pixel during the pedestal taking (resulting 
in a large pedestal RMS), but have moved to another pixel during the calibration run. In this case, the 
number of photo-electrons would result artificially negative. If these 
channels do not show any other deviating behaviour, their number of photo-electrons gets replaced by the 
mean number of photo-electrons in the camera, and the channel is further calibrated as normal.
\end{enumerate}

Moreover, the number of events are counted which have been reconstructed outside a 5 sigma region 
from the mean signal. These events are called ``outliers''. Figure~\ref{fig:outlier} shows a typical 
outlier obtained with the digital filter applied to a low-gain signal.

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{Outlier.eps}
\caption{Example of an event classified as ``un-calibrated''. The histogram has been obtained 
using the digital filter (extractor \#32) applied to a high-intensity blue pulse (run 31772). 
The event marked as ``outlier'' clearly has been mis-reconstructed. It lies outside the 5 sigma 
region from the fitted mean.}
\label{fig:outlier}
\end{figure}

The following figures~\ref{fig:unsuited:5ledsuv},~\ref{fig:unsuited:1leduv},~\ref{fig:unsuited:2ledsgreen}
and~\ref{fig:unsuited:23ledsblue} show the resulting numbers of un-calibrated pixels and events for 
different colours and intensities. 

\par

\begin{figure}[htp]
\centering
\includegraphics[height=0.95\textheight]{UnsuitVsExtractor-5LedsUV-Colour-13.eps}
\caption{Uncalibrated pixels and pixels outside of the Gaussian distribution for a typical calibration  
pulse of UV-light which does not saturate the high-gain readout.}
\label{fig:unsuited:5ledsuv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[height=0.95\textheight]{UnsuitVsExtractor-1LedUV-Colour-04.eps}
\caption{Uncalibrated pixels and pixels outside of the Gaussian distribution for a very low 
intensity pulse.}
\label{fig:unsuited:1leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[height=0.95\textheight]{UnsuitVsExtractor-2LedsGreen-Colour-02.eps}
\caption{Uncalibrated pixels and pixels outside of the Gaussian distribution for a typical green pulse.}
\label{fig:unsuited:2ledsgreen}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[height=0.95\textheight]{UnsuitVsExtractor-23LedsBlue-Colour-00.eps}
\caption{Uncalibrated pixels and pixels outside of the Gaussian distribution for a high-intensity blue pulse.}
\label{fig:unsuited:23ledsblue}
\end{figure}

One can see that in general, big extraction windows raise the 
number of un-calibrated pixels and are thus less stable. Especially for the very low-intensity 
$1Led\,UV$-pulse, the big extraction windows summing 8 or more slices, cannot calibrate more than 50\% 
of the inner pixels (fig.~\ref{fig:unsuited:1leduv}). This is an expected behavior since big windows 
add up more noise which in turn makes the for the small signal more difficult.
\par
In general, one can also find that all ``sliding window''-algorithms (extractors \#17-32) discard 
less pixels than the ``fixed window''-ones (extractors \#1--16). The digital filter with 
the correct weights (extractor \#32) discards the least number of pixels and is also robust against 
slight modifications of its weights (extractors \#28--31). Also the ``spline'' algorithms on small  
windows (extractors \#23--25) discard less pixels than the previous extractors, although slightly more 
than the digital filter.
\par
Particularly in the low-gain channel, 
there is one extractor discarding a too high amount of events which is the 
MExtractFixedWindowPeakSearch. The reason becomes clear when one keeps in mind that this extractor 
defines its extraction window by searching for the highest signal found in a sliding peak search window
 looping only over {\textit{non-saturating pixels}}. In the case of an intense calibration pulse, only 
the dead pixels match this requirement and define thus an alleatory window fluctuating like the noise 
does in these channels. It is clear that one cannot use this extractor for the intense calibration pulses. 
\par
It seems also that the spline algorithm extracting the amplitude of the signal produces an over-proportional
number of excluded events in the low-gain. The same, however in a less significant manner, holds for 
the digital filter with high-low-gain inverted weights. The limit of stability with respect to 
changes  in the pulse form seems to be reached, there.
\par
Concerning the numbers of outliers, one can conclude that in general, the numbers are very low never exceeding
0.25\%. There seems to be the opposite trend of larger windows producing less 
outliers. However, one has to take into account that already more ``unsuited'' pixels have 
been excluded thus cleaning up the sample of pixels somewhat. It seems that the ``digital filter'' and a 
medium-sized ``spline'' (extractors \#25--26) yield the best result except for the outer pixels 
in fig~\ref{fig:unsuited:5ledsuv} where the digital filter produces a worse result than the rest 
of the extractors.
\par
In conclusion, already this first test excludes all extractors with too big window sizes because 
they are not able to extract cleanly small signals produced by about 4 photo-electrons. Moreover, 
some extractors do not reproduce the signals as expected in the low-gain. 
The excluded extractors are:
\begin{itemize}
\item: MExtractFixedWindow Nr. 3--5
\item: MExtractFixedWindowSpline Nr. 6--11
\item: MExtractFixedWindowPeakSearch Nr. 14--16
\item: MExtractTimeAndChargeSlidingWindow Nr. 21--22
\item: MExtractTimeAndChargeSpline Nr. 27
\end{itemize}

The best extractors after this test are:
\begin{itemize}
\item: MExtractFixedWindow Nr. 1--2
\item: MExtractFixedWindowPeakSearch Nr. 13
\item: MExtractTimeAndChargeSlidingWindow Nr. 17--19
\item: MExtractTimeAndChargeSpline Nr. 24--25
\item: MExtractTimeAndChargeDigitalFilter Nr. 28--32
\end{itemize}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\subsubsection{Number of Photo-Electrons \label{sec:photo-electrons}}

Assuming that the readout chain is clean and adds only negligible noise to the one 
introduced by the photo-multiplier itself, one can make the assumption that the variance of the 
true (non-extracted) signal $ST$ is the amplified Poisson variance of the number of photo-electrons, 
multiplied with the excess noise of the photo-multiplier which itself is 
characterized by the excess-noise factor $F$.

\begin{equation}
Var(ST) = F^2 \cdot Var(N_{phe}) \cdot \frac{<ST>^2}{<N_{phe}>^2}
\label{eq:excessnoise}
\end{equation}

After introducing the effect of the night-sky background (eq.~\ref{eq:rmssubtraction}) 
in formula~\ref{eq:excessnoise} and assuming that the number of photo-electrons per event follows a 
Poisson distribution, one obtains an expression to retrieve the mean number of photo-electrons 
impinging on the pixel from the 
mean extracted signal $<SE>$, its variance $Var(SE)$ and the RMS of the extracted signal obtained from 
pure pedestal runs $R$ (see section~\ref{sec:determiner}):

\begin{equation}
<N_{phe}> \approx F^2 \cdot \frac{<SE>^2}{Var(SE) - R^2}
\label{eq:pheffactor}
\end{equation}

In theory, eq.~\ref{eq:pheffactor} must not depend on the extractor! Effectively, we will use it to test the 
quality of our extractors by requiring that a valid extractor yields the same number of photo-electrons 
for all pixels of a same type and does not deviate from the number obtained with other extractors. 
As the camera is flat-fielded, but the number of photo-electrons impinging on an inner and an outer pixel is 
different, we also use the ratio of the mean numbers of photo-electrons from the outer pixels to the one 
obtained from the inner pixels as a test variable. In the ideal case, it should always yield its central 
value of about 2.6$\pm$0.1~\cite{michele-diploma}.
\par
In our case, there is an additional complication due to the fact that the green and blue coloured light pulses 
show secondary pulses which destroy the Poisson behaviour of the number of photo-electrons. We will thus 
have to split our sample of extractors into those being affected by the secondary pulses and those 
being immune to this effect. 
\par
Figures~\ref{fig:phe:5ledsuv},~\ref{fig:phe:1leduv},~\ref{fig:phe:2ledsgreen}~and~\ref{fig:phe:23ledsblue} show 
some of the obtained results. Although one can see an amazing stability for the standard 5\,Leds\,UV pulse, 
there is a considerable difference for all shown non-standard pulses. Especially the pulses from green 
and blue LEDs 
show a clear dependency  of the number of photo-electrons on the extraction window. Only the largest 
extraction windows seem to catch the entire range of (jittering) secondary pulses and get the ratio 
of outer vs. inner pixels right. However, they (obviously) over-estimate the number of photo-electrons 
in the primary pulse.
\par
The strongest discrepancy is observed in the low-gain extraction (fig.~\ref{fig:phe:23ledsblue}) where all 
fixed window extractors essentially fail to reconstruct the correct numbers. This has to do with the fact
that the tail of the high-gain pulse is usually very close to the low-gain one and thus, the extraction range
has to be determined with great precision, what the fixed window extractors fail to do due because of the 
1~FADC slice event-to-event jitter.


\begin{figure}[htp]
\centering
\includegraphics[height=0.92\textheight]{PheVsExtractor-5LedsUV-Colour-13.eps}
\caption{Number of photo-electrons from a typical, not saturating calibration pulse of colour UV, 
reconstructed with each of the tested signal extractors. 
The first plots shows the number of photo-electrons obtained for the inner pixels, the second one 
for the outer pixels and the third shows the ratio of the mean number of photo-electrons for the 
outer pixels divided by the mean number of photo-electrons for the inner pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:phe:5ledsuv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[height=0.92\textheight]{PheVsExtractor-1LedUV-Colour-04.eps}
\caption{Number of photo-electrons from a typical, very low-intensity calibration pulse of colour UV, 
reconstructed with each of the tested signal extractors. 
The first plots shows the number of photo-electrons obtained for the inner pixels, the second one 
for the outer pixels and the third shows the ratio of the mean number of photo-electrons for the 
outer pixels divided by the mean number of photo-electrons for the inner pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:phe:1leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[height=0.92\textheight]{PheVsExtractor-2LedsGreen-Colour-02.eps}
\caption{Number of photo-electrons from a typical, not saturating calibration pulse of colour green, 
reconstructed with each of the tested signal extractors. 
The first plots shows the number of photo-electrons obtained for the inner pixels, the second one 
for the outer pixels and the third shows the ratio of the mean number of photo-electrons for the 
outer pixels divided by the mean number of photo-electrons for the inner pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:phe:2ledsgreen}
\end{figure}


\begin{figure}[htp]
\centering
\includegraphics[height=0.92\textheight]{PheVsExtractor-23LedsBlue-Colour-00.eps}
\caption{Number of photo-electrons from a typical, high-gain saturating calibration pulse of colour blue, 
reconstructed with each of the tested signal extractors. 
The first plots shows the number of photo-electrons obtained for the inner pixels, the second one 
for the outer pixels and the third shows the ratio of the mean number of photo-electrons for the 
outer pixels divided by the mean number of photo-electrons for the inner pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:phe:23ledsblue}
\end{figure}

One can see that all extractors using a large window belong to the class of extractors being affected 
by the secondary pulses. The only exception to this rule is the digital filter which - despite of its 
6 slices extraction window - seems to filter out all the secondary pulses. 
\par
Moreover, one can see in fig.~\ref{fig:phe:1leduv} that all peak searching extractors show the influence of 
the bias at low numbers of photo-electrons. 
\par
The extractor MExtractFixedWindowPeakSearch at low extraction windows apparently yields chronically low 
numbers of photo-electrons. This is due to the fact that the decision to fix the extraction window is 
made sometimes by an inner pixel and sometimes by an outer one since the camera is flat-fielded and the 
pixel carrying the largest non-saturated peak-search window is more or less found by a random signal 
fluctuation. However, inner and outer pixels have a systematic offset of about 0.5 to 1 FADC slices. 
Thus, the extraction fluctuates artificially for one given channel which results in a systematically 
large variance and thus in a systematically low reconstructed number of photo-electrons. This test thus 
excludes the extractors \#11--13.
\par
Moreover, one can see that the extractors applying a small fixed window do not get the ratio of 
photo-electrons correctly between outer to inner pixels for the green and blue pulses. 
\par
The extractor MExtractTimeAndChargeDigitalFilter seems to be stable against modifications in the 
exact form of the weights in the high-gain readout channel since all applied weights yield about 
the same number of photo-electrons and the same ratio of outer vs. inner pixels. This statement does not 
hold any more for the low-gain, as can be seen in figure~\ref{fig:phe:23ledsblue}. There, the application 
of high-gain weights to the low-gain signal (extractors \#30--31) produces a too low number of photo-electrons
and also a too low ratio of outer vs. inner pixels.
\par
All sliding window and spline algorithms yield a stable ratio of outer vs. inner pixels in the low-gain, 
however the effect of raising the number of photo-electrons with the extraction window is very pronounced. 
Note that in figure~\ref{fig:phe:23ledsblue}, the number of photo-electrons rises by about a factor 1.4, 
which is slightly higher than in the case of the high-gain channel (figure~\ref{fig:phe:2ledsgreen}). 
\par
Concluding, there is no fixed window extractor yielding the correct number of photo-electrons 
for the low-gain, except for the largest extraction window of 10 low-gain slices. 
Either the number of photo-electrons itself is wrong or the ratio of outer vs. inner pixels is 
not correct. All sliding window algorithms seem to reproduce the correct numbers if one takes into 
account the after-pulse behaviour of the light pulser itself. The digital filter seems to be 
unstable against exchanging the pulse form to match the slimmer high-gain pulses, though.


\subsubsection{Linearity Tests}

In this section, we test the lineary of the extractors. As the photo-multiplier and the subsequent 
optical transmission devices~\cite{david} is a linear device over a 
wide dynamic range, the number of photo-electrons per charge has to remain constant over the tested 
linearity region. We will show here only examples of extractors which were not already excluded in the 
previous section.
\par
A first test concerns the stability of the conversion factor: mean number of averaged photo-electrons 
per FADC counts over the 
tested intensity region. A much more detailed investigation on the linearity will be shwon in a 
separate TDAS~\cite{tdas-calibration}.


\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-3.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A fixed window extractor on a window size of 6 high-gain and 6 low-gain slices has been used (extractor \#3). }
\label{fig:linear:phevscharge3}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-8.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A fixed window spline extractor on a window size of 6 high-gain and 6 low-gain slices has been used 
(extractor \#8). }
\label{fig:linear:phevscharge8}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-14.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A fixed window peak search extractor on a window size of 6 high-gain and 6 low-gain slices has been used 
(extractor \#14). }
\label{fig:linear:phevscharge14}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-20.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A sliding window extractor on a window size of 6 high-gain and 6 low-gain slices has been used
 (extractor \#20). }
\label{fig:linear:phevscharge20}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-25.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
An integrating spline extractor on a sliding window and a window size of 2 high-gain and 3 low-gain slices 
has been used (extractor \#25). }
\label{fig:linear:phevscharge25}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-27.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
An integrating spline extractor on a sliding window and a window size of 6 high-gain and 7 low-gain slices 
has been used (extractor \#27). }
\label{fig:linear:phevscharge27}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-30.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A digital filter extractor on a window size of 6 high-gain and 6 low-gain slices has been used
 (extractor \#32). }
\label{fig:linear:phevscharge30}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{PheVsCharge-31.eps}
\caption{Example of a the development of the conversion factor FADC counts to photo-electrons for two 
exemplary inner pixels (upper plots) and two exemplary outer ones (lower plots). 
A digital filter extractor on a window size of 4 high-gain and 4 low-gain slices has been used
 (extractor \#32). }
\label{fig:linear:phevscharge31}
\end{figure}



\subsubsection{Time Resolution}

The extractors \#17--32 are able to extract also the arrival time of each pulse. The calibration
delivers a fast-rising pulse, uniform over the camera in signal size and time. 
We estimate the time-uniformity to better 
than 300\,ps, a limit due to the different travel times of the light between inner and outer parts of the
camera. Since the calibraion does not permit a precise measurement of the absolute arrival time, we measure 
the relative arrival time for every channel with respect to a reference channel (usually pixel Nr.\,1):

\begin{equation}
\delta t_i = t_i - t_1
\end{equation}

where $t_i$ denotes the reconstructed arrival time of pixel number $i$ and $t_1$ the reconstructed 
arrival time of the reference pixel nr. 1 (software numbering). For one calibration run, one can then fill 
histograms of $\delta t_i$ for each pixel and fit them to the expected Gaussian distribution. The fits 
yield a mean $\mu(\delta t_i)$, comparable to 
systematic offsets in the signal delay, and a sigma $\sigma(\delta t_i)$, a measure of the 
combined time resolutions of pixel $i$ and pixel 1. Assuming that the PMTs and readout channels are 
of a same kind, we obtain an approximate absolute time resolution of pixel $i$ by:

\begin{equation}
t^{res}_i \approx \sigma(\delta t_i)/sqrt(2)
\end{equation}

Figures~\ref{fig:reltimesinner10leduv} and~\ref{fig:reltimesouter10leduv} show distributions of $\delta t_i$ 
for 
one typical inner pixel and one typical outer pixel and a non-saturating calibration pulse of UV-light, 
obtained with three different extractors. One can see that the first two yield a Gaussian distribution 
to a good approximation, whereas the third extractor shows a three-peak structure and cannot be fitted. 
We discarded that particular extractor for this reason.

\begin{figure}[htp]
\centering
\includegraphics[width=0.3\linewidth]{RelArrTime_Pixel97_10LedUV_Extractor32.eps}
\includegraphics[width=0.32\linewidth]{RelArrTime_Pixel97_10LedUV_Extractor23.eps}
\includegraphics[width=0.32\linewidth]{RelArrTime_Pixel97_10LedUV_Extractor17.eps}
\caption{Example of a two distributions of relative arrival times of an inner pixel with respect to 
the arrival time of the reference pixel Nr. 1. The left plot shows the result using the digital filter
 (extractor \#32), the central plot shows the result obtained with the half-maximum of the spline and the 
right plot the result of the sliding window with a window size of 2 FADC slices (extractor \#17). A 
medium sized UV-pulse (10Leds UV) has been used which does not saturate the high-gain readout channel.}
\label{fig:reltimesinner10leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel400_10LedUV_Extractor32.eps}
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel400_10LedUV_Extractor23.eps}
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel400_10LedUV_Extractor17.eps}
\caption{Example of a two distributions of relative arrival times of an outer pixel with respect to 
the arrival time of the reference pixel Nr. 1. The left plot shows the result using the digital filter
 (extractor \#32), the central plot shows the result obtained with the half-maximum of the spline and the 
right plot the result of the sliding window with a window size of 2 FADC slices (extractor \#17). A 
medium sized UV-pulse (10Leds UV) has been used which does not saturate the high-gain readout channel.}
\label{fig:reltimesouter10leduv}
\end{figure}

Figures~\ref{fig:reltimesinner10ledsblue} and~\ref{fig:reltimesouter10ledsblue} show distributions of 
$<\delta t_i>$ for 
one typical inner and one typical outer pixel and a high-gain-saturating calibration pulse of blue-light, 
obtained with two different extractors. One can see that the first (extractor \#23) yields a Gaussian 
distribution to a good approximation, whereas the second (extractor \#32) shows a two-peak structure 
and cannot be fitted. 
\par
\ldots {\it Unfortunately, this happens for all digital filter extractors in the low-gain. 
The reason is not yet understood, and has to be found by Hendrik... } \ldots
\par

\begin{figure}[htp]
\centering
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel97_10LedBlue_Extractor23.eps}
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel97_10LedBlue_Extractor32.eps}
\caption{Example of a two distributions of relative arrival times of an inner pixel with respect to 
the arrival time of the reference pixel Nr. 1. The left plot shows the result using the half-maximum of the spline (extractor \#23), the right plot shows the result obtained with the digital filter
(extractor \#32). A 
medium sized Blue-pulse (10Leds Blue) has been used which saturates the high-gain readout channel.}
\label{fig:reltimesinner10ledsblue}
\end{figure}



\begin{figure}[htp]
\centering
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel400_10LedBlue_Extractor23.eps}
\includegraphics[width=0.31\linewidth]{RelArrTime_Pixel400_10LedBlue_Extractor32.eps}
\caption{Example of a two distributions of relative arrival times of an outer pixel with respect to 
the arrival time of the reference pixel Nr. 1. The left plot shows the result using the half-maximum of the spline (extractor \#23), the right plot shows the result obtained with the digital filter
(extractor \#32). A 
medium sized Blue-pulse (10Leds Blue) has been used which saturates the high-gain readout channel.}
\label{fig:reltimesouter10ledsblue}
\end{figure}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{UnsuitTimeVsExtractor-5LedsUV-Colour-12.eps}
\caption{Reconstructed arrival time resolutions from a typical, not saturating calibration pulse 
of colour UV, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:5ledsuv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{UnsuitTimeVsExtractor-1LedUV-Colour-04.eps}
\caption{Reconstructed arrival time resolutions from the lowest intensity calibration pulse 
of colour UV (carrying a mean number of 4 photo-electrons), 
reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:1leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{UnsuitTimeVsExtractor-2LedsGreen-Colour-02.eps}
\caption{Reconstructed arrival time resolutions from a typical, not saturating calibration pulse 
of colour Green, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:2ledsgreen}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{UnsuitTimeVsExtractor-23LedsBlue-Colour-00.eps}
\caption{Reconstructed arrival time resolutions from the highest intensity calibration pulse 
of colour blue, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:23ledsblue}
\end{figure}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResExtractor-5LedsUV-Colour-12.eps}
\caption{Reconstructed arrival time resolutions from a typical, not saturating calibration pulse 
of colour UV, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:5ledsuv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResExtractor-1LedUV-Colour-04.eps}
\caption{Reconstructed arrival time resolutions from the lowest intensity calibration pulse 
of colour UV (carrying a mean number of 4 photo-electrons), 
reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:1leduv}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResExtractor-2LedsGreen-Colour-02.eps}
\caption{Reconstructed arrival time resolutions from a typical, not saturating calibration pulse 
of colour Green, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:2ledsgreen}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResExtractor-23LedsBlue-Colour-00.eps}
\caption{Reconstructed arrival time resolutions from the highest intensity calibration pulse 
of colour blue, reconstructed with each of the tested arrival time extractors. 
The first plots shows the time resolutions obtained for the inner pixels, the second one 
for the outer pixels. Points 
denote the mean of all not-excluded pixels, the error bars their RMS.}
\label{fig:time:23ledsblue}
\end{figure}


\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResVsCharge-Area-21.eps}
\caption{Reconstructed mean arrival time resolutions as a function of the extracted mean number of 
photo-electrons for the weighted sliding window with a window size of 8 FADC slices (extractor \#21).
Error bars denote the 
spread (RMS) of the time resolutions over the investigated channels.
The marker colours show the applied 
pulser colour, except for the last (green) point where all three colours were used.}
\label{fig:time:dep20}
\end{figure}

\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResVsCharge-Area-24.eps}
\caption{Reconstructed mean arrival time resolutions as a function of the extracted mean number of 
photo-electrons for the half-maximum searching spline (extractor \#23). Error bars denote the 
spread (RMS) of the time resolutions over the investigated channels.
The marker colours show the applied 
pulser colour, except for the last (green) point where all three colours were used.}
\label{fig:time:dep23}
\end{figure}


\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResVsCharge-Area-30.eps}
\caption{Reconstructed mean arrival time resolutions as a function of the extracted signal 
for the digital filter with UV weights and 6 slices (extractor \#30).  Error bars denote the 
spread (RMS) of the time resolutions over the investigated channels.
The marker colours show the applied 
pulser colour, except for the last (green) point where all three colours were used.}
\label{fig:time:dep30}
\end{figure}


\begin{figure}[htp]
\centering
\includegraphics[width=0.95\linewidth]{TimeResVsCharge-Area-31.eps}
\caption{Reconstructed mean arrival time resolutions as a function of the extracted signal 
for the digital filter with UV weights and 4 slices (extractor \#32).  Error bars denote the 
spread (RMS) of the time resolutions over the investigated channels.
The marker colours show the applied 
pulser colour, except for the last (green) point where all three colours were used.}
\label{fig:time:dep32}
\end{figure}





\clearpage

\subsection{Pulpo Pulses}
\subsection{MC Data}
\subsection{Cosmics Data?}
The results of this subsection are based on the following runs taken
on the 21st of September 2004.
\begin{itemize}
\item{Run 39000}: OffCrab11 at 19.1 degrees zenith angle and 106.2
azimuth.
\item{Run 39182}: CrabNebula at 19.0 degrees zenith angle and 106.0 azimuth.
\end{itemize}

\subsection{Pedestals}


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