Eur. Phys. J. C (2018) 78: 82
https://doi.org/10.1140/epjc/s10052-017-5481-6

Regular Article - Experimental Physics

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

¹ Universität Bern, 3012, Bern, Switzerland
² Brookhaven National Laboratory (BNL), 11973, Upton, NY, USA
³ University of Cambridge, CB3 0HE, Cambridge, UK
⁴ University of Chicago, 60637, Chicago, IL, USA
⁵ University of Cincinnati, 45221, Cincinnati, OH, USA
⁶ Columbia University, 10027, New York, NY, USA
⁷ Fermi National Accelerator Laboratory (FNAL), 60510, Batavia, IL, USA
⁸ Harvard University, 02138, Cambridge, MA, USA
⁹ Illinois Institute of Technology (IIT), 60616, Chicago, IL, USA
¹⁰ Kansas State University (KSU), 66506, Manhattan, KS, USA
¹¹ Lancaster University, LA1 4YW, Lancaster, UK
¹² Los Alamos National Laboratory (LANL), 87545, Los Alamos, NM, USA
¹³ The University of Manchester, M13 9PL, Manchester, UK
¹⁴ Massachusetts Institute of Technology (MIT), 02139, Cambridge, MA, USA
¹⁵ University of Michigan, 48109, Ann Arbor, MI, USA
¹⁶ New Mexico State University (NMSU), 88003, Las Cruces, NM, USA
¹⁷ Otterbein University, 43081, Westerville, OH, USA
¹⁸ University of Oxford, OX1 3RH, Oxford, UK
¹⁹ Pacific Northwest National Laboratory (PNNL), 99352, Richland, WA, USA
²⁰ University of Pittsburgh, 15260, Pittsburgh, PA, USA
²¹ Saint Mary’s University of Minnesota, 55987, Winona, MN, USA
²² SLAC National Accelerator Laboratory, 94025, Menlo Park, CA, USA
²³ Syracuse University, 13244, Syracuse, NY, USA
²⁴ Tel Aviv University, 69978, Tel Aviv, Israel
²⁵ University of Tennessee, 37996, Knoxville, TN, USA
²⁶ University of Texas, 76019, Arlington, TX, USA
²⁷ TUBITAK Space Technologies Research Institute, METU Campus, 06800, Ankara, Turkey
²⁸ Center for Neutrino Physics, Virginia Tech, 24061, Blacksburg, VA, USA
²⁹ Yale University, 06520, New Haven, CT, USA

^ct marshall@hep.phy.cam.ac.uk

Received: 23 August 2017
Accepted: 18 December 2017
Published online: 29 January 2018

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.