1 / 30

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.

diallo
Download Presentation

Reconstructing Neutrino Interactions in Liquid Argon TPCs Ben Newell Steve Dennis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related