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

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

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

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

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

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

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

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

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

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

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

12. 12 CARLA in 3D

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

14. 14 Early results

15. 15

16. 16

17. 17

18. 18

19. 19

20. 20

21. 21

22. 22

23. 23 Efficiency of CARLA

24. 24 2D: Efficiency of reconstructing correct 2 tracks

25. 25 2D: Variation of efficiency with breaking angle

26. 26 2D: Variation of efficiency with voxel size

27. 27 3D: Opening Angle Variance

28. 28 3D: Opening Angle Variance

29. 29 CARLA in 3D

30. 30 Future Developments Improvements to filtering Improvements to filtering Documentation Documentation User Interface User Interface