125 lines
5.7 KiB
TeX
125 lines
5.7 KiB
TeX
%\subsubsection{plan}
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%\begin{itemize}
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%\item two stage: alcapana then rootana
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%\item alcapana is online analysis as well as primary midas -> root conversion
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%\item rootana is the main offline analysis
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%\item Waveform Analysis
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%\begin{itemize}
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%\item Pulse Candidate Finder
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%\item Amplitude: MaxBin
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%\item Timing: Constant Fraction
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%\item Calibration: Timing offsets, Pedestals, Energy scales
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%\end{itemize}
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%\item Event Correlating: Fast and Slow coincidence
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%\item Event Correlating: Muon events
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%\end{itemize}
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\begin{figure}[htbp]
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\centering
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\includegraphics[width=0.7\textwidth]{figs/WaveformAnalysis.png}
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\caption{Illustration of the waveform analysis techniques used in Rootana.}
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\label{fig:waveform_reco_demo}
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\end{figure}
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%The analaysis of the Alcap data is split between two main stages, known as
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%Alcapana and Rootana.
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The AlCap DAQ delivers data as compressed MIDAS files. These are then
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handled in two stages. In the first stage (Alcapana),
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some preliminary analysis is
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performed and the MIDAS files unpacked into a ROOT format. These are then
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passed through the second stage (Rootana) for physics analysis.
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\subsubsection{Alcapana}
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The MIDAS files produced by the DAQ contain both the digitised
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waveform outputs
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and the run-time DAQ configuration. Alcapana is the first point in the
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processing chain to look at this data and performs two roles: 1) it
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provides some preliminary, semi-online analysis, 2) it converts the
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MIDAS data into a ROOT format.
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The analysis produces simple histograms of quantities such as amplitude,
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timing, and pulse island length. It also creates persistency-style
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overlays of each
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waveform in the event. These histograms are placed in an output file which can
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then be loaded into a simple ROOT-based GUI where results can be displayed
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rapidly and help to understand the data quality.
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%Much of this was implemented during the run and by the end the alcapana
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%analysis and displays formed a strong and user-friendly tool-kit.
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Since each digitiser is run in a self-trigger mode within a MIDAS block, the
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output waveforms contain, in principle, a single pulse stored as a vector of
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ADC samples. The waveform digitisers stamp each pulse with a trigger time-stamp relative to the
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start of the MIDAS block. Each of these time-stamped ADC vectors is referred
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to as a Pulse Island. Occasionally a
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real pulse may be split over two Pulse Islands. Therefore, during the
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unpacking in Alcapana Pulse Islands are with adjacent time-stamps are
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checked to concatenated into a single pulse when necessary.
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The treatment of the $\mu$PC detector is slightly different as it is readout using a discriminator and TDC, and so does not use the same waveform format for raw data.
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\subsubsection{Rootana}
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Most of the physics analysis is performed in Rootana. The framework is
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designed to be easily configured by dividing the analysis into
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modules which can be called and reconfigured at run-time through a
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configuration file. Most processing chains begin with waveform analysis so
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the module for this delegates the work to runtime selectable `generators' which
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implement the actual waveform analysis. Then the program searches for
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correlations or coincidences between different detectors in order
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to produce the final physics results.
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\subsubsection*{Waveform Analysis}
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The general approach to waveform analysis
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uses the following methodology:
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\begin{description}
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\item [1. Find Pulse Candidates:] Remove pulse islands which are just noise and
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divide pile-up pulses into two separate pulse islands.
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%
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%\item [2. Subtract Pedestal]
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%
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\item [2. Amplitude and Time reconstruction:] Use the Max Bin method (sometimes
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Peak Sample method) to extract the amplitude followed by a Constant Fraction Timing
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process using a linear interpolation between the two bins closest to the
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peak-sample which crosses a given fraction of the pulse's amplitude. See Fig.
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\ref{fig:waveform_reco_demo} for an illustration of these techniques.
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%
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\item [3. Apply Calibration Constants:] Account for cable delays and
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similar effects in
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the timing and convert the amplitude to energy. The constants
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are obtained from a prior data pass which calculates the timing and
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pedestal data, or from a dedicated calibration datasets in the case of energy
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constants.
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\end{description}
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We are investigating alternative methods of waveform analysis using
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pulse-averaging in order to produce a template followed by fitting or
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convolution of the
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original waveform. Since the analysis shown below has not used these
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methods,
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they will not be described here.
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\subsubsection*{Pulse Correlation}
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There are two types of pulse correlation used in the analysis:
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1) correlation of
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pulses between the fast and slow filtered read-out channels of a
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single detector,
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and 2) correlation of pulses between all detectors. The first method
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provides a
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cross check on the data quality, but results in a reduction of the overall
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dynamic range of the detector since the correlation is only meaningful for
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pulses with energies within the intersection of the individual
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dynamic ranges of each channel.
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The second method is used when pulses are correlated between
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detectors. It
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is implemented
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by restructuring the dataset into what are known as ``Muon Events''. Individual
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pulses on the $\mu$Sc are used as a reference point and all pulses which occur
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within a certain time window (typically 15~\textmu s) are collected together into
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a single muon event. Should two or more $\mu$Sc pulses occur within this window,
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the event is marked as a muon pile-up event. It is then trivial to scan
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over each muon event, applying various cuts, in order to build a
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spectrum of interest.
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Since the bulk of the correlation work is done by inspecting the timing of
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pulses on different channels, the accuracy of the time offsets due to
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cable delays is particularly important.
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