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Reconstructing Neutrino Interactions inReconstructing Neutrino Interactions inLiquid Argon TPCsLiquid Argon TPCs
Ben Newell Ben Newell Steve DennisSteve Dennis
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OutlineOutline
LAr-TPCsLAr-TPCs
Automation desirableAutomation desirable
Algorithmic recognition Algorithmic recognition
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Cellular AutomataCellular Automata
Conway's 'Game of Life'Conway's 'Game of Life'
Local rulesLocal rules
Cell states update simultaneouslyCell states update simultaneously
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CATS – The Cellular Automaton at HERA-BCATS – The Cellular Automaton at HERA-B
HERA-B experiment uses eight 'superlayers'HERA-B experiment uses eight 'superlayers'
Create 'track segments' between layersCreate 'track segments' between layers
Cellular automaton on track segmentsCellular automaton on track segments
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Cellular Automata for Track ReconstructionCellular Automata for Track Reconstruction
Cells have an index – initially 1Cells have an index – initially 1
Local – only neighboursLocal – only neighbours
Common endpointCommon endpoint
'Breaking angle''Breaking angle'
Principal DirectionPrincipal Direction
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The CA Algorithm – Forward PassThe CA Algorithm – Forward Pass
For each cell:For each cell:
Look for leftward neighboursLook for leftward neighbours Check if any have same indexCheck if any have same index Mark index to updateMark index to update
Update all indicesUpdate all indices
RepeatRepeat
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The CA Algorithm – Reverse PassThe CA Algorithm – Reverse Pass
Start at highest index cellStart at highest index cell
Run to cell of index 1 using steps of 1Run to cell of index 1 using steps of 1
Mark cells usedMark cells used
Repeat with unused cellsRepeat with unused cells
Build all possible pathsBuild all possible paths
Result: List of track candidatesResult: List of track candidates
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CARLA – A Cellular Automaton for Reconstruction CARLA – A Cellular Automaton for Reconstruction in Liquid Argonin Liquid Argon
Implemented in PythonImplemented in Python
Extra steps required to suit our needsExtra steps required to suit our needs
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Clustering the DataClustering the Data
LAr-TPC resolution ~mmLAr-TPC resolution ~mm
Thousands of voxels in principal directionThousands of voxels in principal direction
Performance problemsPerformance problems
ClusteringClustering
Voxel sizeVoxel size Clustering orthogonal to principal directionClustering orthogonal to principal direction Reject 'lone' pointsReject 'lone' points
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Post-ProcessingPost-Processing
Problems:Problems:
BreakingBreaking KinksKinks ClonesClones
Filtering by shared pointsFiltering by shared points
Track cleaningTrack cleaning
BreakerBreaker MergerMerger
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Generalisation to 3DGeneralisation to 3D
Simple to work in higher dimensionsSimple to work in higher dimensions
DirectionalityDirectionality
May miss tracksMay miss tracks
Solution: permute the axes and run on eachSolution: permute the axes and run on each
Recombine the resultsRecombine the results
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Parameters for reconstructionParameters for reconstruction
Voxel sizeVoxel size Clustering radiusClustering radius Cell toleranceCell tolerance Filtering toleranceFiltering tolerance Breaking AngleBreaking Angle MergerMerger
Direction ToleranceDirection Tolerance Distance ToleranceDistance Tolerance
BreakerBreaker Correlation ToleranceCorrelation Tolerance Segment lengthSegment length
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2D: Efficiency of reconstructing correct 2 tracks2D: Efficiency of reconstructing correct 2 tracks