The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
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10052_2017_Article_5481.pdf
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Author(s)
Acciarri, R.
Adams, C.
An, R.
Anthony, J.
Asaadi, J.
Auger, M.
Bagby, L.
Balasubramanian, S.
Baller, B.
Barnes, C.
Date Issued
January 2018
Journal
The European Physical Journal C
Publisher
Springer Berlin Heidelberg
Citation
Acciarri, R., et al. “The Pandora Multi-Algorithm Approach to Automated Pattern Recognition of Cosmic-Ray Muon and Neutrino Events in the MicroBooNE Detector.” The European Physical Journal C, vol. 78, no. 1, Jan. 2018.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjc/s10052-017-5481-6
