Published January 1, 2018 | Version v1
Journal article Open

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Creators

  • 1. FNAL, Batavia, IL 60510 USA
  • 2. IIT, Chicago, IL 60616 USA
  • 3. Univ Cambridge, Cambridge CB3 0HE, England
  • 4. Univ Texas Arlington, Arlington, TX 76019 USA
  • 5. Univ Bern, CH-3012 Bern, Switzerland
  • 6. Yale Univ, New Haven, CT 06520 USA
  • 7. Univ Michigan, Ann Arbor, MI 48109 USA
  • 8. Univ Oxford, Oxford OX1 3RH, England
  • 9. TUBITAK Space Technol Res Inst, METU Campus, TR-06800 Ankara, Turkey
  • 10. BNL, Upton, NY 11973 USA
  • 11. Univ Lancaster, Lancaster LA1 4YW, England
  • 12. Kansas State Univ, Manhattan, KS 66506 USA
  • 13. Columbia Univ, New York, NY 10027 USA

Description

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

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