Reconstructing Neutrino Interactions in
Liquid Argon TPCs
Ben Newell
Steve Dennis
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Outline
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LAr-TPCs
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Automation desirable
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Algorithmic recognition
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Cellular Automata
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Conway's 'Game of Life'
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Local rules
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Cell states update simultaneously
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CATS – The Cellular Automaton at HERA-B
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HERA-B experiment uses eight 'superlayers'
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Create 'track segments' between layers
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Cellular automaton on track segments
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Cellular Automata for Track Reconstruction
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Cells have an index – initially 1
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Local – only neighbours
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Common endpoint
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'Breaking angle'
Principal Direction
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The CA Algorithm – Forward Pass
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For each cell:
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Look for leftward neighbours
Check if any have same index
Mark index to update
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Update all indices
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Repeat
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The CA Algorithm – Reverse Pass
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Start at highest index cell
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Run to cell of index 1 using steps of 1
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Mark cells used
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Repeat with unused cells
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Build all possible paths
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Result: List of track candidates
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CARLA – A Cellular Automaton for Reconstruction in
Liquid Argon
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Implemented in Python
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Extra steps required to suit our needs
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Clustering the Data
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LAr-TPC resolution ~mm
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Thousands of voxels in principal direction
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Performance problems
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Clustering
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Voxel size
Clustering orthogonal to principal direction
Reject 'lone' points
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Post-Processing
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Problems:
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Breaking
Kinks
Clones
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Filtering by shared points
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Track cleaning
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Breaker
Merger
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Generalisation to 3D
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Simple to work in higher dimensions
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Directionality
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May miss tracks
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Solution: permute the axes and run on each
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Recombine the results
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CARLA in 3D
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Parameters for reconstruction
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Voxel size
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Clustering radius
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Cell tolerance
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Filtering tolerance
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Breaking Angle
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Merger
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Direction Tolerance
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Distance Tolerance
Breaker
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Correlation Tolerance
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Segment length
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Early results
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Efficiency of CARLA
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2D: Efficiency of reconstructing correct 2 tracks
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2D: Variation of efficiency with breaking angle
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2D: Variation of efficiency with voxel size
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3D: Opening Angle Variance
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3D: Opening Angle Variance
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CARLA in 3D
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Future Developments
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Improvements to filtering
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Documentation
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User Interface
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