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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
- Source :
- Springer Berlin Heidelberg, Acciarri, R, Adams, C, An, R, Anthony, J, Asaadi, J, Auger, M, Bagby, L, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Bass, M, Bay, F, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Carls, B, Castillo Fernandez, R, Cavanna, F, Chen, H, Church, E, Cianci, D, Cohen, E, Collin, G H, Conrad, J M, Convery, M, Crespo-Anadón, J I, Del Tutto, M, Devitt, D, Dytman, S, Eberly, B, Ereditato, A, Escudero Sanchez, L, Esquivel, J, Fadeeva, A A, Fleming, B T, Foreman, W, Furmanski, A P, Garcia-Gamez, D, Garvey, G T, Genty, V, Goeldi, D, Gollapinni, S, Graf, N, Gramellini, E, Greenlee, H, Grosso, R, Guenette, R, Hackenburg, A, Hamilton, P, Hen, O, Hewes, J, Hill, C, Ho, J, Horton-Smith, G, Hourlier, A, Huang, E C, James, C, Jan de Vries, J, Jen, C M, Jiang, L, Johnson, R A, Joshi, J, Jostlein, H, Kaleko, D, Karagiorgi, G, Ketchum, W, Kirby, B, Kirby, M, Kobilarcik, T, Kreslo, I, Laube, A, Li, Y, Lister, A, Littlejohn, B R, Lockwitz, S, Lorca, D, Louis, W C, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Mariani, C, Marshall, J, Martinez Caicedo, D A, Meddage, V, Miceli, T, Mills, G B, Moon, J, Mooney, M, Moore, C D, Mousseau, J, Murrells, R, Naples, D, Nienaber, P, Nowak, J, Palamara, O, Paolone, V, Papavassiliou, V, Pate, S F, Pavlovic, Z, Piasetzky, E, Porzio, D, Pulliam, G, Qian, X, Raaf, J L, Rafique, A, Rochester, L, Rudolf von Rohr, C, Russell, B, Schmitz, D W, Schukraft, A, Seligman, W, Shaevitz, M H, Sinclair, J, Smith, A, Snider, E L, Soderberg, M, Söldner-Rembold, S, Soleti, S R, Spentzouris, P, Spitz, J, St. John, J, Strauss, T, Szelc, A M, Tagg, N, Terao, K, Thomson, M, Toups, M, Tsai, Y T, Tufanli, S, Usher, T, Vandepontseele, W, Vandewater, R G, Viren, B, Weber, M, Wickremasinghe, D A, Wolbers, S, Wongjirad, T, Woodruff, K, Yang, T, Yates, L, Zeller, G P, Zennamo, J & Zhang, C 2018, ' The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector ', European Physical Journal C, vol. 78, no. 1, 82 . https://doi.org/10.1140/epjc/s10052-017-5481-6, The European Physical Journal. C, Particles and Fields, European Physical Journal, BASE-Bielefeld Academic Search Engine, European Physical Journal C: Particles and Fields, Vol 78, Iss 1, Pp 1-25 (2018), Eur. Phys. J. C, 1 (2018) pp. 82, The European Physical Journal C
- Publication Year :
- 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.
- Subjects :
- Physics and Astronomy (miscellaneous)
Exploit
Regular Article - Experimental Physics
lcsh:Astrophysics
computer.software_genre
Network topology
01 natural sciences
Task (project management)
lcsh:QB460-466
0103 physical sciences
lcsh:Nuclear and particle physics. Atomic energy. Radioactivity
Detectors and Experimental Techniques
010306 general physics
Engineering (miscellaneous)
QC
Physics
010308 nuclear & particles physics
business.industry
Event (computing)
Detector
Software development
Pattern recognition
Advanced software [3]
Pattern recognition (psychology)
lcsh:QC770-798
Data mining
Artificial intelligence
Neutrino
business
computer
Algorithm
Subjects
Details
- ISSN :
- 14346052 and 14346044
- Volume :
- 78
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- European Physical Journal C
- Accession number :
- edsair.doi.dedup.....9493626550049f1ca37a6db5ef0e77a7