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

R. Acciarri; C. Adams; R. An; J. Anthony; J. Asaadi; M. Auger; L. Bagby; S. Balasubramanian; B. Baller; C. Barnes; G. Barr; M. Bass; F. Bay; M. Bishai; A. Blake; T. Bolton; L. Camilleri; D. Caratelli; B. Carls; R. Castillo Fernandez; F. Cavanna; H. Chen; E. Church; D. Cianci; E. Cohen; G. H. Collin; J. M. Conrad; M. Convery; J. I. Crespo-Anadón; M. Del Tutto; D. Devitt; S. Dytman; B. Eberly; A. Ereditato; L. Escudero Sanchez; J. Esquivel; A. A. Fadeeva; B. T. Fleming; W. Foreman; A. P. Furmanski; D. Garcia-Gamez; G. T. Garvey; V. Genty; D. Goeldi; S. Gollapinni; N. Graf; E. Gramellini; H. Greenlee; R. Grosso; R. Guenette; A. Hackenburg; P. Hamilton; O. Hen; J. Hewes; C. Hill; J. Ho; G. Horton-Smith; A. Hourlier; E. C. Huang; C. James; J. Jan de Vries; C. M. Jen; L. Jiang; R. A. Johnson; J. Joshi; H. Jostlein; D. Kaleko; G. Karagiorgi; W. Ketchum; B. Kirby; M. Kirby; T. Kobilarcik; I. Kreslo; A. Laube; Y. Li; A. Lister; B. R. Littlejohn; S. Lockwitz; D. Lorca; W. C. Louis; M. Luethi; B. Lundberg; X. Luo; A. Marchionni; C. Mariani; J. Marshall; D. A. Martinez Caicedo; V. Meddage; T. Miceli; G. B. Mills; J. Moon; M. Mooney; C. D. Moore; J. Mousseau; R. Murrells; D. Naples; P. Nienaber; J. Nowak; O. Palamara; V. Paolone; V. Papavassiliou; S. F. Pate; Z. Pavlovic; E. Piasetzky; D. Porzio; G. Pulliam; X. Qian; J. L. Raaf; A. Rafique; L. Rochester; C. Rudolf von Rohr; B. Russell; D. W. Schmitz; A. Schukraft; W. Seligman; M. H. Shaevitz; J. Sinclair; A. Smith; E. L. Snider; M. Soderberg; S. Söldner-Rembold; S. R. Soleti; P. Spentzouris; J. Spitz; J. St. John; T. Strauss; A. M. Szelc; N. Tagg; K. Terao; M. Thomson; M. Toups; Y. T. Tsai; S. Tufanli; T. Usher; W. Vandepontseele; R. G. Vandewater; B. Viren; M. Weber; D. A. Wickremasinghe; S. Wolbers; T. Wongjirad; K. Woodruff; T. Yang; L. Yates; G. P. Zeller; J. Zennamo; C. Zhang

doi:10.1140/epjc/s10052-017-5481-6

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

R. Acciarri, C. Adams, R. An, J. Anthony, J. Asaadi, M. Auger, L. Bagby, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, M. Bass, F. Bay, M. Bishai, A. Blake, T. Bolton, L. Camilleri, D. Caratelli, B. Carls, R. Castillo FernandezF. Cavanna, H. Chen, E. Church, D. Cianci, E. Cohen, G. H. Collin, J. M. Conrad, M. Convery, J. I. Crespo-Anadón, M. Del Tutto, D. Devitt, S. Dytman, B. Eberly, A. Ereditato, L. Escudero Sanchez, J. Esquivel, A. A. Fadeeva, B. T. Fleming, W. Foreman, A. P. Furmanski, D. Garcia-Gamez, G. T. Garvey, V. Genty, D. Goeldi, S. Gollapinni, N. Graf, E. Gramellini, H. Greenlee, R. Grosso, R. Guenette, A. Hackenburg, P. Hamilton, O. Hen, J. Hewes, C. Hill, J. Ho, G. Horton-Smith, A. Hourlier, E. C. Huang, C. James, J. Jan de Vries, C. M. Jen, L. Jiang, R. A. Johnson, J. Joshi, H. Jostlein, D. Kaleko, G. Karagiorgi, W. Ketchum, B. Kirby, M. Kirby, T. Kobilarcik, I. Kreslo, A. Laube, Y. Li, A. Lister, B. R. Littlejohn, S. Lockwitz, D. Lorca, W. C. Louis, M. Luethi, B. Lundberg, X. Luo, A. Marchionni, C. Mariani, J. Marshall^*, D. A. Martinez Caicedo, V. Meddage, T. Miceli, G. B. Mills, J. Moon, M. Mooney, C. D. Moore, J. Mousseau, R. Murrells, D. Naples, P. Nienaber, J. Nowak, O. Palamara, V. Paolone, V. Papavassiliou, S. F. Pate, Z. Pavlovic, E. Piasetzky, D. Porzio, G. Pulliam, X. Qian, J. L. Raaf, A. Rafique, L. Rochester, C. Rudolf von Rohr, B. Russell, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Söldner-Rembold, S. R. Soleti, P. Spentzouris, J. Spitz, J. St. John, T. Strauss, A. M. Szelc, N. Tagg, K. Terao, M. Thomson, M. Toups, Y. T. Tsai, S. Tufanli, T. Usher, W. Vandepontseele, R. G. Vandewater, B. Viren, M. Weber, D. A. Wickremasinghe, S. Wolbers, T. Wongjirad, K. Woodruff, T. Yang, L. Yates, G. P. Zeller, J. Zennamo, C. Zhang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	82
Journal	European Physical Journal C
Volume	78
Issue number	1
Early online date	29 Jan 2018
DOIs	https://doi.org/10.1140/epjc/s10052-017-5481-6
Publication status	Published - 2018

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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., ... 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, 78(1), Article 82. https://doi.org/10.1140/epjc/s10052-017-5481-6

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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.",

author = "R. Acciarri and C. Adams 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 {Castillo Fernandez}, R. and F. Cavanna and H. Chen and E. Church and D. Cianci and E. Cohen and Collin, {G. H.} and Conrad, {J. M.} and M. Convery and Crespo-Anad{\'o}n, {J. I.} and {Del Tutto}, M. and D. Devitt and S. Dytman and B. Eberly and A. Ereditato and {Escudero Sanchez}, L. and J. Esquivel and Fadeeva, {A. A.} and Fleming, {B. T.} and W. Foreman and Furmanski, {A. P.} and D. Garcia-Gamez 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 R. Guenette and A. Hackenburg and P. Hamilton and O. Hen and J. Hewes and C. Hill and J. Ho and G. Horton-Smith and A. Hourlier and Huang, {E. C.} and C. James and {Jan de Vries}, J. and Jen, {C. M.} and L. Jiang and Johnson, {R. A.} and J. Joshi and H. Jostlein and D. Kaleko and G. Karagiorgi and W. Ketchum and B. Kirby and M. Kirby and T. Kobilarcik and I. Kreslo and A. Laube and Y. Li and A. Lister and Littlejohn, {B. R.} and S. Lockwitz and D. Lorca and Louis, {W. C.} and M. Luethi and B. Lundberg and X. Luo and A. Marchionni and C. Mariani and J. Marshall and {Martinez Caicedo}, {D. A.} and V. Meddage and T. Miceli and Mills, {G. B.} and J. Moon and M. Mooney and Moore, {C. D.} and J. Mousseau and R. Murrells and D. Naples and P. Nienaber and J. Nowak and O. Palamara and V. Paolone and V. Papavassiliou and Pate, {S. F.} and Z. Pavlovic and E. Piasetzky and D. Porzio and G. Pulliam and X. Qian and Raaf, {J. L.} and A. Rafique and L. Rochester and {Rudolf von Rohr}, C. and B. Russell and Schmitz, {D. W.} and A. Schukraft and W. Seligman and Shaevitz, {M. H.} and J. Sinclair and A. Smith and Snider, {E. L.} and M. Soderberg and S. S{\"o}ldner-Rembold and Soleti, {S. R.} and P. Spentzouris and J. Spitz and {St. John}, J. and T. Strauss and Szelc, {A. M.} and N. Tagg and K. Terao and M. Thomson and M. Toups and Tsai, {Y. T.} and S. Tufanli and T. Usher and W. Vandepontseele and Vandewater, {R. G.} and B. Viren and M. Weber and Wickremasinghe, {D. A.} and S. Wolbers and T. Wongjirad and K. Woodruff and T. Yang and L. Yates and Zeller, {G. P.} and J. Zennamo and C. Zhang",

year = "2018",

doi = "10.1140/epjc/s10052-017-5481-6",

language = "English",

volume = "78",

journal = "European Physical Journal C",

issn = "1434-6044",

publisher = "Springer Nature",

number = "1",

}

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, GH, Conrad, JM, Convery, M, Crespo-Anadón, JI, Del Tutto, M, Devitt, D, Dytman, S, Eberly, B, Ereditato, A, Escudero Sanchez, L, Esquivel, J, Fadeeva, AA, Fleming, BT, Foreman, W, Furmanski, AP, Garcia-Gamez, D, Garvey, GT, 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, EC, James, C, Jan de Vries, J, Jen, CM, Jiang, L, Johnson, RA, 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, BR, Lockwitz, S, Lorca, D, Louis, WC, Luethi, M, Lundberg, B, Luo, X, Marchionni, A, Mariani, C, Marshall, J, Martinez Caicedo, DA, Meddage, V, Miceli, T, Mills, GB, Moon, J, Mooney, M, Moore, CD, Mousseau, J, Murrells, R, Naples, D, Nienaber, P, Nowak, J, Palamara, O, Paolone, V, Papavassiliou, V, Pate, SF, Pavlovic, Z, Piasetzky, E, Porzio, D, Pulliam, G, Qian, X, Raaf, JL, Rafique, A, Rochester, L, Rudolf von Rohr, C, Russell, B, Schmitz, DW, Schukraft, A, Seligman, W, Shaevitz, MH, Sinclair, J, Smith, A, Snider, EL, Soderberg, M, Söldner-Rembold, S, Soleti, SR, Spentzouris, P, Spitz, J, St. John, J, Strauss, T, Szelc, AM, Tagg, N, Terao, K, Thomson, M, Toups, M, Tsai, YT, Tufanli, S, Usher, T, Vandepontseele, W, Vandewater, RG, Viren, B, Weber, M, Wickremasinghe, DA, Wolbers, S, Wongjirad, T, Woodruff, K, Yang, T, Yates, L, Zeller, GP, 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

TY - JOUR

T1 - The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

AU - Acciarri, R.

AU - Adams, C.

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 - Castillo Fernandez, R.

AU - Cavanna, F.

AU - Chen, H.

AU - Church, E.

AU - Cianci, D.

AU - Cohen, E.

AU - Collin, G. H.

AU - Conrad, J. M.

AU - Convery, M.

AU - Crespo-Anadón, J. I.

AU - Del Tutto, M.

AU - Devitt, D.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Escudero Sanchez, L.

AU - Esquivel, J.

AU - Fadeeva, A. A.

AU - Fleming, B. T.

AU - Foreman, W.

AU - Furmanski, A. P.

AU - Garcia-Gamez, 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 - Guenette, R.

AU - Hackenburg, A.

AU - Hamilton, P.

AU - Hen, O.

AU - Hewes, J.

AU - Hill, C.

AU - Ho, J.

AU - Horton-Smith, G.

AU - Hourlier, A.

AU - Huang, E. C.

AU - James, C.

AU - Jan de Vries, J.

AU - Jen, C. M.

AU - Jiang, L.

AU - Johnson, R. A.

AU - Joshi, J.

AU - Jostlein, H.

AU - Kaleko, D.

AU - Karagiorgi, G.

AU - Ketchum, W.

AU - Kirby, B.

AU - Kirby, M.

AU - Kobilarcik, T.

AU - Kreslo, I.

AU - Laube, A.

AU - Li, Y.

AU - Lister, A.

AU - Littlejohn, B. R.

AU - Lockwitz, S.

AU - Lorca, D.

AU - Louis, W. C.

AU - Luethi, M.

AU - Lundberg, B.

AU - Luo, X.

AU - Marchionni, A.

AU - Mariani, C.

AU - Marshall, J.

AU - Martinez Caicedo, D. A.

AU - Meddage, V.

AU - Miceli, T.

AU - Mills, G. B.

AU - Moon, J.

AU - Mooney, M.

AU - Moore, C. D.

AU - Mousseau, J.

AU - Murrells, R.

AU - Naples, D.

AU - Nienaber, P.

AU - Nowak, J.

AU - Palamara, O.

AU - Paolone, V.

AU - Papavassiliou, V.

AU - Pate, S. F.

AU - Pavlovic, Z.

AU - Piasetzky, E.

AU - Porzio, D.

AU - Pulliam, G.

AU - Qian, X.

AU - Raaf, J. L.

AU - Rafique, A.

AU - Rochester, L.

AU - Rudolf von Rohr, C.

AU - Russell, B.

AU - Schmitz, D. W.

AU - Schukraft, A.

AU - Seligman, W.

AU - Shaevitz, M. H.

AU - Sinclair, J.

AU - Smith, A.

AU - Snider, E. L.

AU - Soderberg, M.

AU - Söldner-Rembold, S.

AU - Soleti, S. R.

AU - Spentzouris, P.

AU - Spitz, J.

AU - St. John, J.

AU - Strauss, T.

AU - Szelc, A. M.

AU - Tagg, N.

AU - Terao, K.

AU - Thomson, M.

AU - Toups, M.

AU - Tsai, Y. T.

AU - Tufanli, S.

AU - Usher, T.

AU - Vandepontseele, W.

AU - Vandewater, R. G.

AU - Viren, B.

AU - Weber, M.

AU - Wickremasinghe, D. A.

AU - Wolbers, S.

AU - Wongjirad, T.

AU - Woodruff, K.

AU - Yang, T.

AU - Yates, L.

AU - Zeller, G. P.

AU - Zennamo, J.

AU - Zhang, C.

PY - 2018

Y1 - 2018

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.

UR - http://www.scopus.com/inward/record.url?scp=85041345614&partnerID=8YFLogxK

U2 - 10.1140/epjc/s10052-017-5481-6

DO - 10.1140/epjc/s10052-017-5481-6

M3 - Article

AN - SCOPUS:85041345614

SN - 1434-6044

VL - 78

JO - European Physical Journal C

JF - European Physical Journal C

IS - 1

M1 - 82

ER -

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

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