21.1. SVD Reconstruction

The SVD reconstruction starts with SVDShaperDigits and SVDEventInfo, SVDClusters are created on both sides of the sensors and then combined into SVDSpacePoints.

Several algorithms are available for cluster charge, time and position reconstruction, and they are specified as parameters of the SVDClusterizer module (chargeAlgorithm{3/6}Samples, timeAlgorithm{3/6}Samples, positionAlgorithm{3/6}Samples).

When we reconstruct simulated events, fudge factors are applied to the cluster time and position in order to reproduce the resolution that we measure in data, see SVDMCClusterPositionFudge and SVDMCClusterTimeFudge.

Use the following python function if you want to add the SVD reconstruction to your steering file:

svd.__init__.add_svd_reconstruction(path, isROIsimulation=False, createRecoDigits=False, applyMasking=False)

Adds the SVD reconstruction to the path.

Reconstruction starts with SVDShaperDigits and SVDEventInfo and provides SVDClusters and SVDSpacePoints.

Parameters

• path – add the modules to this basf2 path.

• isROIsimulation – SVD reconstruction can be run during simulation in order to simulate the PXD Data Reduction with ROI finding.

• createRecoDigits – if True, SVDRecoDigits are created.

• applyMasking – if True, hot strips found in SVDHotStripsCalibration are masked.

21.1.1. Clustering

The first step of reconstruction after unpacking (or simulation) is the clustering, i.e. grouping adjacent strips into RawCluster. All acquired strips are good for clustering since the minimum value of SNR corresponds to the online zero-suppression cut: SNR > 3. A RawCluster is promoted to SVDCluster if there is at least one strip in the cluster with SNR > 5. The parameters for clustering are stored in the SVDClustering DBObject.

21.1.2. Cluster Charge Reconstruction

The cluster charge is a measurement of the charge released in the sensor by the charged particle, and collected on one side of the sensor.

The default algorithm used to compute cluster charge is the MaxSample: the highest strip sample for each strip in the cluster represents the strip charge (in ADC). This value is converted in \(e^{-}\) using the SVDPulseShapeCalibrations DBObject. The cluster charge is computed as the sum of the strips charges in \(e^{-}\).

We have two alternative algorithms to compute the cluster charge that can be selected by setting the SVDClusterizer parameter chargeAlgorithm{3/6}Samples:

1. SumSamples: the strip charge is evaluated as the sum of the 6 samples in ADC, converted in \(e^{-}\) with the help of the SVDPulseShapeCalibrations DBObject and then the cluster charge is the computed as the sum of the strip charges.

2. ELS3: first, all strips in the cluster are summed sample by sample. Then, the 3 best consecutive summed-samples are found with the MaxSum algorithm and the maximum \(A\) of the theoretical CR-RC waveform (with \(\tau = 55\ \rm ns\)):

   \[a(t) = A \frac{t-t_{\rm raw}}{\tau}\exp{\left(1 - \frac{t-t_{\rm raw}}{\tau}\right)}\]

   is computed with a simple system of equations:

   \[A = \frac{(e_{\tau}^{-1} - e_{\tau}^{3}) a_{1} + (2+e_{\tau}^{2})a_{2} - (1+2e_{\tau}^{2})a_{0}}{\Delta t/\tau \exp{(1+t_{\rm raw}/\tau)}(1+4e_{\tau}^{2}+e_{\tau}^{4})}\]

   where \(e_{\tau} = e^{- \frac{\Delta t}{\tau}}\), \(\Delta t \simeq 31.44\) ns is the sampling period of the APV readout chip, \(t_{\rm raw}\) is the raw ELS3 time, and \(a_{j}\) is the amplitude of j-th summed-sample.

   Finally the charge is converted in \(e^{-}\) with the the SVDPulseShapeCalibrations DBObject.

See also

All three algorithms are implemented in the svd/reconstruction/SVDClusterCharge class.

The MaxSum Algorithm:

1. find the two consecutive samples for which \((a_{i} + a_{i+1})\) represents the maximum, where \(a_{j}\) is the amplitude of j-th sample.

2. the three chosen samples are: \(a_{i-1}, a_{i}, a_{i+1}\), with \(i-1 = FF\), the FirstFrame.

3. in case in which \(i = 0\), the three chosen samples are \(a_0 , a_1 , a_2\) with \(FF=0\).

21.1.3. Cluster Time Reconstruction

The cluster time is a measurement of the time of the hit (with respect to the trigger signal).

The default algorithm used to compute cluster time is the CoG3: first, all strips in the cluster are summed sample by sample, and the 3 best consecutive summed-samples are determined using the MaxSum algorithm. Then, the raw cluster time is the average of the 3 best summed-samples time with the sample charge:

\[t_{\rm cluster}^{\rm raw} = \Delta t \cdot \frac{\sum_{i=0}^{i<3}i\ a_i}{\sum_{i=0}^{i<3} a_i}.\]

where the \(\Delta t \simeq 31.44\) ns is the sampling period of the APV readout chip and \(a_{j}\) is the amplitude of j-th summed-sample.

The raw time is finally calibrated with a third order polynomial stored in the SVDCoG3SampleTimeCalibration DBObject, see Hit Time Calibration for more details on the calibration.

We have two alternative algorithms to compute the cluster time that can be selected by setting the SVDClusterizer parameter timeAlgorithm{3/6}Samples.

1. CoG6: the cluster time is the average of the strips time weighted with the strip charge. The raw strip time is computed as the average of the sample time weighted with the sample amplitude:

   \[t_{\rm strip}^{\rm raw} = \Delta t \cdot \frac{\sum_{i=0}^{i<6}i\ a_i}{\sum_{i=0}^{i<6} a_i}.\]

   where the \(\Delta t \simeq 31.44\) ns is the sampling period of the APV readout chip and \(a_{j}\) is the amplitude of j-th sample. The raw strip time is calibrated with a third order polynomial stored in the SVDCoGTimeCalibration DBObject.

2. ELS3: as for CoG3, all strips in the cluster are summed sample by sample and the MaxSum algorithm is applied, and then the raw cluster time is computed with a simple system of equations as \(t_{\rm raw}\) of the theoretical CR-RC waveform:

   \[t_{\rm cluster}^{\rm raw} = - \Delta t \cdot \frac{2e_{\tau}^{4} + w e_{\tau}^{2}}{1 - e_{\tau}^{4}-w(2+e_{\tau}^{2})}\]

   where \(e_{\tau} = e^{- \frac{\Delta t}{\tau}}\), \(\Delta t \simeq 31.44\) ns is the sampling period of the APV readout chip, \(w = \frac{a_{0}-2 e_{\tau}^{-2} a_{2}}{2a_{0} + e_{\tau}^{-1} a_{1}}\) and \(a_{j}\) is the amplitude of j-th summed-sample.

   The raw time is finally calibrated with a third order polynomial stored in the SVDELS3SampleTimeCalibration DBObject.

See also

All three algorithms are implemented in the svd/reconstruction/SVDClusterTime class.

Note

The hit time expressed in the trigger reference \(t_{\rm hit}\) is:

\[t_{\rm hit} = f(t_{\rm raw}) + r_{\rm shift} + \frac{\Delta t}{4} \cdot (3 - TB + 4\ FF)\]

where \(f(t_{\rm raw})\) is the calibrated time, \(r_{\rm shift}\) is the relative shift among 3-sample and 6-sample event (= 0 in 6-sample events), \(\Delta t \simeq 31.44\) ns is the sampling period of the APV readout chip, \(TB\) is the TriggerBin and \(FF\) is the FirstFrame.

Shifting of SVD-cluster-time based on Cluster-size

The mean of the cluster-time distribution shifts with the cluster size, as the strips of the clusters with lower amplitude, which are at the edge, delays in time. This effect is not simulated and it appears only in data. To compensate this, improving cluster time resolution in data and data-simulation agreement, a shift is added to the calibrated time. The values are stored in SVDClusterTimeShifter DBObject.

21.1.4. Cluster Position Reconstruction

The algorithm to determine the cluster position depends on the cluster size, i.e. the number of strips forming the cluster.

For one-strip clusters the position is the position of the strip, i.e. the position of the center of the readout implant.

For more-than-one strip clusters we have two algorithms:

1. center-of-gravity CoG, the cluster position is computed averaging the strip position weighted with the strip charge:

\[x_{\rm cluster} = \frac{\sum_{\rm strips} x_i S_i}{\sum_{\rm strips} S_i}.\]

where \(x_i\) is the strip position and \(S_i\) is the strip charge.

2. analog-head-tail AHT:

\[x_{\rm cluster} = \frac{1}{2} \left[x_{\rm head} + x_{\rm tail} + p \frac{S_{\rm head} - S_{\rm tail}}{S_{\rm center}} \right].\]

where \(p\) is the readout pitch, \(x_{\rm head/tail}\) (\(S_{\rm head/tail}\)) are the position (charge) of the two strips at the edge of the cluster and \(S_{\rm center}\) is the average strip charge after removing the strips at the edge, \(S_{\rm center} = \frac{S_{\rm tot} - S_{\rm head} - S_{\rm tail}}{\rm size - 2}\) with \(S_{\rm tot}\) is the total cluster charge.

The available algorithms to determine the strip charge for the position computation are the same available for clusters, strips are considered as one-strip clusters. To choose the strip charge reconstruction algorithm for cluster position computation use the SVDClusterizer parameter stripChargeAlgorithm{3/6}Samples. The default algorithm is the MaxSample.

In the current default reconstruction the CoG is used for cluster size > 1 (AHT is not used). Indeed, the SVDClusterizer supports the following position reconstruction algorithms (that can be passed as string, see the SVDClusterizer parameter positionAlgorithm{3/6}Samples parameter)

1. CoGOnly (current default): the CoG is used for all cluster sizes \(\ge2\) (AHT is not used), error scale factors (= 1 for data and MC) are stored in SVDCoGOnlyErrorScaleFactors.

2. OldDefault: CoG for cluster size = 2 and AHT for cluster sizes > 2 (error scale factors stored in SVDOldDefaultErrorScaleFactors

Note

For CoGOnly, position errors are stored SVDCoGOnlyPositionError.

See also

All algorithms are implemented in the svd/reconstruction/SVDClusterPosition class.

21.1.5. Creation of Clusters in disabled-APV regions

In case one or more APV readout chips are disabled during data taking, a fake cluster is created in the middle of the region in order not to loose the information of the hit on the other side of the sensor. For more details see SVDMissingAPVsClusterCreator.

21.1.6. SVD Cluster Grouping

Clusters are grouped event-by event based on their time distribution. Clusters created by charged particles coming to the same e+e- collision are near in time, and will be grouped together.

Method in SVDTimeGrouping module:

Steps	Description
Preparing Histogram	Creating a 1D histogram of given range [-160, 160] ns and bin-width of 1/2 ns. (default tRangeLow=-160, tRangeHigh=160, rebinningFactor=2)
Filling Cluster Time	For each cluster, a normalized Gaussian is filled in the above histogram, centered at the cluster-time. The width of the Gaussian is parsed from the hard-coded values in the header depending on the side of the sensor and the number of strips in the cluster. To save time, only the bins within 3 sigma of the center is calculated and filled. (default fillSigmaN=3)
Finding Peaks	Once all the Gaussian entries are filled, the correlated-clusters form a big Gaussian (which will be called group hereafter). The group-finding is performed in the following method. Maximum bin is found and a Gaussian is fitted in the range of [-5, 5] ns keeping the bin as center. (default fitRangeHalfWidth=7) The group info (integral, center, width) is stored. The fitted Gaussian is then subtracted from the histogram to find the next significant peak/group. To save time, value of the Gaussian for the bins only within 5 sigma are calculated and subtracted from those bins. (default removeSigmaN=7) Maximum bin is found again and checked whether this peak is above threshold ( > 0.05 x firstGroup). (default fracThreshold=0.05) If above threshold, then the process is repeated again. Search is stopped if 20 groups are found. (default maxGroups=20)
Creating Dummy Groups	If the number of groups found is less than maxGroups then, dummy groups are inserted to increase the total groups to maxGroups. This helps in the sorting process described in the following.
Sorting the Groups	Naturally, the groups are sorted by their prominence. But in an event, background might be higher than signal, causing the background peak occupying the first position. So there is a need for sorting the groups. It is done in two stages: Background: All the groups with centers outside the signal range [-50,50], are moved towards the end of the queue. (default expectedSignalTimeMin=-50, expectedSignalTimeMax=50) These groups are then sorted in reverse, that means, the probability of being background is max at groupID = 19(or max) and decreases with group number decreasing. Signal: Signal groups are naturally sorted based on integral at this point. But sometimes a background group, near signal may be higher than the signal group. Now the probability of finding a signal group near 0 is greater. So the signal groups are sorted with an exponential weight with width 30 ns. (default expectedSignalTimeCenter=0, signalLifetime=30) So, the probability of being signal is max at groupID = 0 and decreases with group number increasing.
Signal Group Selection	Depending on the type of study, we may choose the first group to be accepted for space-point-creation. (default numberOfSignalGroups=1)
formSingleSignalGroup	If this flag is enabled, all the groups are given same groupId = 0.
Assign GroupId to SVD-Clusters	All the clusters are now compared against the surviving groups. The clusters within 5 sigma of a group center are then assigned the groupId and groupInfo. Hence, one cluster shared between two groups can have two groupId. (default acceptSigmaN=7)

Payloads related to SVDTimeGrouping:

1. SVDRecoConfiguration : The switiching ON/OFF of SVDTimeGrouping is controlled by this DBObject.

2. SVDTimeGroupingConfiguration : All the parameters used by this module is stored in this DBObject.

21.1.7. SpacePoint Creation

All clusters on one side of each sensor are combined with all clusters on the other side. Certain combinations of clusters can be excluded based on the hit time, the two available cuts are:

1. exclude SpacePoints in which at least one cluster has hit time below a certain threshold:

   \[t_{u/v} > t_{\rm min}\]

2. exclude SpacePoints in which at least one cluster has hit time far in time w.r.t. trigger (t=0) above a certain threshold:

   \[|t_{u/v}| < t_{\rm max}\]

3. exclude SpacePoints in which the time difference of the two clusters exceeds a certain threshold:

   \[|t_u - t_v| < \Delta t_{\rm max}\]

4. exclude SpacePoints formed by clusters belonging to different SVD groups.

The choice of the cut and of the threshold is stored in the SVDHitTimeSelection for what concerns 1,2,3; while to enable the selection based on grouping, the module parameter useSVDGroupInfo should be used.

SpacePoints are not created if they exceed a certain threshold defined in the numMaxSpacePoints parameter of the SVDSpacePointCreator

21.1.8. Strip Reconstruction (Optional)

The SVDRecoDigitCreator reconstructs raw strips, creating one SVDRecoDigit for each SVDShaperDigit.

Strip reconstruction is not called in the default SVD reconstruction. It is anyway necessary for DQM or other performance studies. Use the following python function if you want to add the SVD strip reconstruction to your steering file:

svd.__init__.add_svd_create_recodigits(path, recocreatorName='SVDRecoDigitCreator', shaperDigitsName='')

Adds the strip reconstruction to the path.

Produce SVDRecoDigits from SVDShaperDigits.

Parameters

• path – add the modules to this basf2 path.

• recocreatorName – name of the module.

• shaperDigitsName – name of the SVDShaperDigits StoreArray.

The charge and time algorithm available for clusters are also available for strips (considered as one-strip clusters). To choose the algorithm use the SVDRecoDigitCreator parameter chargeAlgorithm{3/6}Samples, timeAlgorithm{3/6}Samples.

21.1.9. Reconstruction Modules

This is a list of the svd modules used for reconstruction.

SVDClusterizer

This module produces SVDClusters from SVDShaperDigits, providing 1-D hit position, charge and time on SVD sensors.

Package

svd

Library

libsvdReconstruction.so

Parameters

• AdjacentSN (float, default=3.0)

  minimum SNR for strips to be considered for clustering. Overwritten by the dbobject, unless you set useDB = False.

• ClusterSN (float, default=0.0)

  minimum value of the SNR of the cluster. Overwritten by the dbobject, unless you set useDB = False.

• Clusters (str, default=’’)

  SVDCluster collection name.

• EventInfo (str, default=’SVDEventInfo’)

  SVDEventInfo collection name.

• MCParticles (str, default=’’)

  MCParticles collection name.

• SVDTrueHits (str, default=’’)

  TrueHit collection name.

• SeedSN (float, default=5.0)

  minimum SNR for strips to be considered as cluster seed. Overwritten by the dbobject, unless you set useDB = False.

• ShaperDigits (str, default=’’)

  SVDShaperDigits collection name.

• chargeAlgorithm3Samples (str, default=’not set’)

  cluster-charge reconstruction algorithm for 3-sample DAQ mode: MaxSample (default), SumSamples, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• chargeAlgorithm6Samples (str, default=’not set’)

  cluster-charge reconstruction algorithm for 6-sample DAQ mode: MaxSample (default), SumSamples, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• positionAlgorithm3Samples (str, default=’not set’)

  cluster-position reconstruction algorithm for 3-sample DAQ mode: old (default), CoGOnly. Overwritten by the dbobject, unless you set useDB = False.

• positionAlgorithm6Samples (str, default=’not set’)

  cluster-position reconstruction algorithm for 6-sample DAQ mode: old (default), CoGOnly. Overwritten by the dbobject, unless you set useDB = False.

• returnClusterRawTime (bool, default=False)

  if True, returns the raw cluster time (to be used for time calibration).

• shiftSVDClusterTime (bool, default=True)

  if True, applies SVDCluster time shift based on cluster-size.

• stripChargeAlgorithm3Samples (str, default=’not set’)

  strip-charge reconstruction algorithm used for cluster position reconstruction for the 3-sample DAQ mode: dontdo = not done, MaxSample, SumSamples, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• stripChargeAlgorithm6Samples (str, default=’not set’)

  strip-charge reconstruction algorithm used for cluster position reconstruction for the 6-sample DAQ mode: dontdo = not done, MaxSample, SumSamples, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• stripTimeAlgorithm3Samples (str, default=’not set’)

  strip-time reconstruction algorithm used for cluster position reconstruction for the 3-sample DAQ mode: dontdo = not done (default), CoG6 = 6-sample CoG, CoG3 = 3-sample CoG, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• stripTimeAlgorithm6Samples (str, default=’not set’)

  strip-time reconstruction algorithm used for cluster position reconstruction for the 6-sample DAQ mode: dontdo = not done (default), CoG6 = 6-sample CoG, CoG3 = 3-sample CoG, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• timeAlgorithm3Samples (str, default=’not set’)

  cluster-time reconstruction algorithm for the 3-sample DAQ mode: CoG6 = 6-sample CoG, CoG3 = 3-sample CoG (default), ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• timeAlgorithm6Samples (str, default=’not set’)

  cluster-time reconstruction algorithm for the 6-sample DAQ mode: CoG6 = 6-sample CoG (default), CoG3 = 3-sample CoG, ELS3 = 3-sample ELS. Overwritten by the dbobject, unless you set useDB = False.

• useDB (bool, default=True)

  if False, use clustering and reconstruction configuration module parameters

SVDMissingAPVsClusterCreator

This module produces clusters in the middle of a region read by a disabled APV. It can be run only after the SVDClusterizer because it does not register the SVDClusters StoreArray in the DataStore, but only add clusters.

Package

svd

Library

libsvdReconstruction.so

Parameters

• Clusters (str, default=’’)

  SVDCluster collection name

• SNR (float, default=15.0)

  fake-cluster SNR

• charge (float, default=20000.0)

  fake-cluster charge (in e-)

• firstFrame (int, default=0)

  first frame, needed to build the fake-cluster

• nFakeClusters (int, default=4)

  number of fake clusters equally distributed in the dead area

• seedCharge (float, default=10000.0)

  fake-cluster seed charge (in e-)

• size (int, default=128)

  fake-cluster size

• time (float, default=0.0)

  fake-cluster time

• timeError (float, default=10.0)

  fake-cluster time error

SVDRecoDigitCreator

This module reconstructs SVDShaperDigit in SVDRecoDigit, i.e. calibrated strip with reconstructed charge and time.

Package

svd

Library

libsvdReconstruction.so

Parameters

• Clusters (str, default=’’)

  SVDCluster collection name.

• RecoDigits (str, default=’’)

  SVDRecoDigits collection name.

• ShaperDigits (str, default=’’)

  SVDShaperDigits collection name.

• chargeAlgorithm3Samples (str, default=’inRecoDBObject’)

  choose charge algorithm for 3-sample DAQ mode: MaxSample (default), SumSamples, ELS3 = 3-sample ELS

• chargeAlgorithm6Samples (str, default=’inRecoDBObject’)

  choose charge algorithm for 6-sample DAQ mode: MaxSample (default), SumSamples, ELS3 = 3-sample ELS

• timeAlgorithm3Samples (str, default=’inRecoDBObject’)

  strip-time reconstruction algorithm for the 3-sample DAQ mode: CoG6 = 6-sample CoG, CoG3 = 3-sample CoG (default), ELS3 = 3-sample ELS

• timeAlgorithm6Samples (str, default=’inRecoDBObject’)

  strip-time reconstruction algorithm for the 6-sample DAQ mode: CoG6 = 6-sample CoG (default), CoG3 = 3-sample CoG, ELS3 = 3-sample ELS

• useDB (bool, default=True)

  if false use clustering and reconstruction configuration module parameters

SVDSpacePointCreator

Imports Clusters of the SVD detector and converts them to spacePoints.

Package

svd

Library

libsvdSpacePointCreator.so

Parameters

• EventLevelTrackingInfoName (str, default=’’)

  EventLevelTrackingInfo collection name

• MinClusterTime (float, default=-20.0)

  clusters with time below this value are not considered to make spacePoints.

• NameOfInstance (str, default=’’)

  allows the user to set an identifier for this module. Usefull if one wants to use several instances of that module

• OnlySingleClusterSpacePoints (bool, default=False)

  standard is false. If activated, the module will not try to find combinations of U and V clusters for the SVD any more

• SVDClusters (str, default=’’)

  SVDCluster collection name

• SVDEventInfo (str, default=’SVDEventInfo’)

  SVDEventInfo collection name.

• SpacePoints (str, default=’SVDSpacePoints’)

  SpacePoints collection name

• forceGroupingFromDB (bool, default=True)

  use SVDRecoConfiguration from DB

• formSingleSignalGroup (bool, default=False)

  Form a single super-group.

• inputPDF (str, default=’/data/svd/spacePointQICalibration.root’)

  Path containing pdf root file

• numMaxSpacePoints (unsigned int, default=70000)

  Maximum number of SpacePoints allowed in an event, above this threshold no SpacePoint will be created

• numberOfSignalGroups (int, default=1)

  Number of groups expected to contain the signal clusters.

• useDBForSNRFraction (bool, default=True)

  if False, use configuration module parameters for SVDSPacePointSNRFractionSelector

• useLegacyNaming (bool, default=True)

  Use old PDF name convention?

• useParamFromDB (bool, default=True)

  use SVDTimeGroupingConfiguration from DB

• useQualityEstimator (bool, default=False)

  Standard is true. If turned off spacepoints will not be assigned a quality in their pairing.

• useSVDGroupInfoIn3Sample (bool, default=False)

  Use SVD group info to reject combinations from clusters belonging to different groups in 3-sample DAQ mode

• useSVDGroupInfoIn6Sample (bool, default=False)

  Use SVD group info to reject combinations from clusters belonging to different groups in 6-sample DAQ mode

• useSVDSpacePointSNRFractionFor3Samples (bool, default=False)

  Use SVDSpacePointSNRFractionSelector to apply a selection on combinations of clusters in 3-sample DAQ mode

• useSVDSpacePointSNRFractionFor6Samples (bool, default=False)

  Use SVDSpacePointSNRFractionSelector to apply a selection on combinations of clusters in 6-sample DAQ mode

SVDTimeGrouping

Assigns the time-group Id to SVD clusters.

Package

svd

Library

libsvdTimeGrouping.so

Parameters

• SVDClusters (str, default=’’)

  SVDCluster collection name

• SVDEventInfo (str, default=’SVDEventInfo’)

  SVDEventInfo collection name.

• acceptSigmaN (float, default=7.0)

  Accept clusters upto N sigma.

• expectedSignalTimeCenter (float, default=0.0)

  Expected time of the signal [ns].

• expectedSignalTimeMax (float, default=50.0)

  Expected high range of signal hits [ns].

• expectedSignalTimeMin (float, default=-50.0)

  Expected low range of signal hits [ns].

• fillSigmaN (float, default=3.0)

  Number of Gaussian sigmas (= hardcoded resolutions) used to fill the time histogram for each cluster.

• fitRangeHalfWidth (float, default=5.0)

  half width of the range in which the fit for the peak-search is performed [ns].

• forceGroupingFromDB (bool, default=True)

  use SVDRecoConfiguration from DB

• fracThreshold (float, default=0.05000000074505806)

  Minimum fraction of candidates in a peak (wrt to the highest peak) considered for fitting in the peak-search.

• includeOutOfRangeClusters (bool, default=True)

  Assign groups to under and overflow.

• isEnabledIn3Samples (bool, default=False)

  if true, module is enabled for 3-sample DAQ mode

• isEnabledIn6Samples (bool, default=False)

  if true, module is enabled for 6-sample DAQ mode

• maxGroups (int, default=20)

  Maximum number of groups to be accepted.

• maxSigma (float, default=15.0)

  Upper limit of cluster time sigma for the fit for the peak-search [ns].

• minSigma (float, default=1.0)

  Lower limit of cluster time sigma for the fit for the peak-search [ns].

• rebinningFactor (int, default=2)

  Time bin width is 1/rebinningFactor ns. Disables the module if set zero

• removeSigmaN (float, default=7.0)

  Evaluate and remove gauss upto N sigma.

• signalLifetime (float, default=30.0)

  Group prominence is weighted with exponential weight with a lifetime defined by this parameter [ns].

• tRangeHigh (float, default=160.0)

  This sets the x+ range of histogram [ns].

• tRangeLow (float, default=-160.0)

  This sets the x- range of histogram [ns].

• useParamFromDB (bool, default=True)

  use SVDTimeGroupingConfiguration from DB

• writeGroupInfo (bool, default=True)

  Write group info into SVDClusters.

SVDUnpacker

Produce SVDShaperDigits from RawSVD. NOTE: only zero-suppressed mode is currently supported!

Package

svd

Library

libsvdUnpacker.so

Parameters

• FADCTriggerNumberOffset (int, default=0)

  number to be added to the FADC trigger number to match the main trigger number

• PrintRawData (bool, default=False)

  Printing Raw data words for debugging

• SVDEventInfo (str, default=’’)

  Name of the SVDEventInfo object

• UnpackerErrorRate (int, default=1000)

  Unpacker will print one error every UnpackerErrorRate

• badMappingFatal (bool, default=False)

  Throw B2FATAL if there’s a wrong payload in the database

• killDigitsFromUpsetAPVs (bool, default=False)

  Delete digits from upset APVs

• rawSVDListName (str, default=’’)

  Name of the raw SVD List

• shutUpFTBError (int, default=-1)

  if >0 is the number of reported FTB header ERRORs before quiet operations. If <0 full log produced.

• silentlyAppend (bool, default=False)

  Append digits to a pre-existing non-empty storeArray

• softwarePipelineAddressEmulation (bool, default=True)

  Estimate emulated pipeline address

• svdDAQDiagnosticsListName (str, default=’’)

  Name of the DAQDiagnostics list

• svdShaperDigitListName (str, default=’’)

  Name of the SVDShaperDigits list