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

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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector. / Blake, A.; Devitt, D.; Lister, A.; Nowak, J.; MicroBooNE Collaboration.

In: European Physical Journal C: Particles and Fields, Vol. 78, 82, 29.01.2018.

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@article{75ef35e7be10414ca33459620173448f,
title = "The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector",
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.",
keywords = "hep-ex, physics.data-an",
author = "MicroBooNE collaboration and R. Acciarri and R. An and J. Anthony and J. Asaadi and M. Auger and L. Bagby and S. Balasubramanian and B. Baller and C. Barnes and G. Barr and M. Bass and F. Bay and M. Bishai and A. Blake and T. Bolton and L. Camilleri and D. Caratelli and B. Carls and Fernandez, {R. Castillo} and F. Cavanna and E. Church and D. Cianci and E. Cohen and Collin, {G. H.} and Conrad, {J. M.} and M. Convery and Crespo-Anadon, {J. I.} and Tutto, {M. Del} and D. Devitt and S. Dytman and B. Eberly and A. Ereditato and Sanchez, {L. Escudero} and J. Esquivel and Fadeeva, {A. A.} and Fleming, {B. T.} and W. Foreman and Furmanski, {A. P.} and D. Garcia-Gomez and Garvey, {G. T.} and V. Genty and D. Goeldi and S. Gollapinni and N. Graf and E. Gramellini and H. Greenlee and R. Grosso and A. Lister and J. Nowak and {MicroBooNE Collaboration}",
year = "2018",
month = jan,
day = "29",
doi = "10.1140/epjc/s10052-017-5481-6",
language = "English",
volume = "78",
journal = "European Physical Journal C: Particles and Fields",
issn = "1434-6044",
publisher = "SPRINGER",

}

RIS

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 -