Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
AU - collaboration, MicroBooNE
AU - Acciarri, R.
AU - An, R.
AU - Anthony, J.
AU - Asaadi, J.
AU - Auger, M.
AU - Bagby, L.
AU - Balasubramanian, S.
AU - Baller, B.
AU - Barnes, C.
AU - Barr, G.
AU - Bass, M.
AU - Bay, F.
AU - Bishai, M.
AU - Blake, A.
AU - Bolton, T.
AU - Camilleri, L.
AU - Caratelli, D.
AU - Carls, B.
AU - Fernandez, R. Castillo
AU - Cavanna, F.
AU - Church, E.
AU - Cianci, D.
AU - Cohen, E.
AU - Collin, G. H.
AU - Conrad, J. M.
AU - Convery, M.
AU - Crespo-Anadon, J. I.
AU - Tutto, M. Del
AU - Devitt, D.
AU - Dytman, S.
AU - Eberly, B.
AU - Ereditato, A.
AU - Sanchez, L. Escudero
AU - Esquivel, J.
AU - Fadeeva, A. A.
AU - Fleming, B. T.
AU - Foreman, W.
AU - Furmanski, A. P.
AU - Garcia-Gomez, D.
AU - Garvey, G. T.
AU - Genty, V.
AU - Goeldi, D.
AU - Gollapinni, S.
AU - Graf, N.
AU - Gramellini, E.
AU - Greenlee, H.
AU - Grosso, R.
AU - Lister, A.
AU - Nowak, J.
AU - MicroBooNE Collaboration
PY - 2018/1/29
Y1 - 2018/1/29
N2 - 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.
AB - 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.
KW - hep-ex
KW - physics.data-an
U2 - 10.1140/epjc/s10052-017-5481-6
DO - 10.1140/epjc/s10052-017-5481-6
M3 - Journal article
VL - 78
JO - European Physical Journal C: Particles and Fields
JF - European Physical Journal C: Particles and Fields
SN - 1434-6044
M1 - 82
ER -