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Reconstructing Neutrino Interactions in Liquid Argon TPCs Ben Newell Steve Dennis

Reconstructing Neutrino Interactions in Liquid Argon TPCs Ben Newell Steve Dennis. Outline. LAr-TPCs Automation desirable Algorithmic recognition. Cellular Automata. Conway's 'Game of Life' Local rules Cell states update simultaneously. CATS – The Cellular Automaton at HERA-B.

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Reconstructing Neutrino Interactions in Liquid Argon TPCs Ben Newell Steve Dennis

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  1. Reconstructing Neutrino Interactions in Liquid Argon TPCs Ben Newell Steve Dennis

  2. Outline • LAr-TPCs • Automation desirable • Algorithmic recognition

  3. Cellular Automata • Conway's 'Game of Life' • Local rules • Cell states update simultaneously

  4. CATS – The Cellular Automaton at HERA-B • HERA-B experiment uses eight 'superlayers' • Create 'track segments' between layers • Cellular automaton on track segments

  5. Cellular Automata for Track Reconstruction • Cells have an index – initially 1 • Local – only neighbours • Common endpoint • 'Breaking angle' • Principal Direction

  6. The CA Algorithm – Forward Pass • For each cell: • Look for leftward neighbours • Check if any have same index • Mark index to update • Update all indices • Repeat

  7. The CA Algorithm – Reverse Pass • Start at highest index cell • Run to cell of index 1 using steps of 1 • Mark cells used • Repeat with unused cells • Build all possible paths • Result: List of track candidates

  8. CARLA – A Cellular Automaton for Reconstruction in Liquid Argon • Implemented in Python • Extra steps required to suit our needs

  9. Clustering the Data • LAr-TPC resolution ~mm • Thousands of voxels in principal direction • Performance problems • Clustering • Voxel size • Clustering orthogonal to principal direction • Reject 'lone' points

  10. Post-Processing • Problems: • Breaking • Kinks • Clones • Filtering by shared points • Track cleaning • Breaker • Merger

  11. Generalisation to 3D • Simple to work in higher dimensions • Directionality • May miss tracks • Solution: permute the axes and run on each • Recombine the results

  12. CARLA in 3D

  13. Parameters for reconstruction • Voxel size • Clustering radius • Cell tolerance • Filtering tolerance • Breaking Angle • Merger • Direction Tolerance • Distance Tolerance • Breaker • Correlation Tolerance • Segment length

  14. Early results

  15. Efficiency of CARLA

  16. 2D: Efficiency of reconstructing correct 2 tracks

  17. 2D: Variation of efficiency with breaking angle

  18. 2D: Variation of efficiency with voxel size

  19. 3D: Opening Angle Variance

  20. 3D: Opening Angle Variance

  21. CARLA in 3D

  22. Future Developments • Improvements to filtering • Documentation • User Interface

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