Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

(The MicroBooNE Collaboration); P. Abratenko; M. Alrashed; R. An; J. Anthony; J. Asaadi; A. Ashkenazi; S. Balasubramanian; B. Baller; C. Barnes; G. Barr; V. Basque; L. Bathe-Peters; O. Benevides Rodrigues; S. Berkman; A. Bhanderi; A. Bhat; M. Bishai; A. Blake; T. Bolton; L. Camilleri; D. Caratelli; I. Caro Terrazas; R. Castillo Fernandez; F. Cavanna; G. Cerati; Y. Chen; E. Church; D. Cianci; J. M. Conrad; M. Convery; L. Cooper-Troendle; J. I. Crespo-Anadón; M. Del Tutto; S. R. Dennis; D. Devitt; R. Diurba; R. Dorrill; K. Duffy; S. Dytman; B. Eberly; A. Ereditato; J. J. Evans; G. A. Fiorentini Aguirre; R. S. Fitzpatrick; B. T. Fleming; N. Foppiani; D. Franco; A. P. Furmanski; D. Garcia-Gamez; S. Gardiner

doi:10.1103/PhysRevD.103.052012

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

(The MicroBooNE Collaboration), P. Abratenko, M. Alrashed, R. An, J. Anthony, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. BoltonL. Camilleri, D. Caratelli, I. Caro Terrazas, R. Castillo Fernandez, F. Cavanna, G. Cerati, Y. Chen, E. Church, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, S. R. Dennis, D. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, J. J. Evans, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. Garcia-Gamez, S. Gardiner

Physics and Astronomy (Twin Cities)

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

12 Scopus citations

Abstract

We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥99%. For full neutrino interaction simulations, the time for processing one image is ≈0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

Original language	English (US)
Article number	052012
Journal	Physical Review D
Volume	103
Issue number	5
DOIs	https://doi.org/10.1103/PhysRevD.103.052012
State	Published - Mar 26 2021

Bibliographical note

Funding Information:
This document was prepared by the MicroBooNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; the European Union’s Horizon 2020 Marie Sklodowska-Curie Actions; and The Royal Society (United Kingdom). Additional support for the laser calibration system and cosmic ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland.

Publisher Copyright:
© 2021 American Physical Society.

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10.1103/PhysRevD.103.052012

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(The MicroBooNE Collaboration), Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., Bathe-Peters, L., Benevides Rodrigues, O., Berkman, S., Bhanderi, A., Bhat, A., Bishai, M., Blake, A., ... Gardiner, S. (2021). Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE. Physical Review D, 103(5), [052012]. https://doi.org/10.1103/PhysRevD.103.052012

(The MicroBooNE Collaboration), Abratenko, P, Alrashed, M, An, R, Anthony, J, Asaadi, J, Ashkenazi, A, Balasubramanian, S, Baller, B, Barnes, C, Barr, G, Basque, V, Bathe-Peters, L, Benevides Rodrigues, O, Berkman, S, Bhanderi, A, Bhat, A, Bishai, M, Blake, A, Bolton, T, Camilleri, L, Caratelli, D, Caro Terrazas, I, Castillo Fernandez, R, Cavanna, F, Cerati, G, Chen, Y, Church, E, Cianci, D, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Del Tutto, M, Dennis, SR, Devitt, D, Diurba, R, Dorrill, R, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Evans, JJ, Fiorentini Aguirre, GA, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, Garcia-Gamez, D & Gardiner, S 2021, 'Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE', Physical Review D, vol. 103, no. 5, 052012. https://doi.org/10.1103/PhysRevD.103.052012

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title = "Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE",

abstract = "We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥99%. For full neutrino interaction simulations, the time for processing one image is ≈0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.",

author = "{(The MicroBooNE Collaboration)} and P. Abratenko and M. Alrashed and R. An and J. Anthony and J. Asaadi and A. Ashkenazi and S. Balasubramanian and B. Baller and C. Barnes and G. Barr and V. Basque and L. Bathe-Peters and {Benevides Rodrigues}, O. and S. Berkman and A. Bhanderi and A. Bhat and M. Bishai and A. Blake and T. Bolton and L. Camilleri and D. Caratelli and {Caro Terrazas}, I. and {Castillo Fernandez}, R. and F. Cavanna and G. Cerati and Y. Chen and E. Church and D. Cianci and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anad{\'o}n, {J. I.} and {Del Tutto}, M. and Dennis, {S. R.} and D. Devitt and R. Diurba and R. Dorrill and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and Evans, {J. J.} and {Fiorentini Aguirre}, {G. A.} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and N. Foppiani and D. Franco and Furmanski, {A. P.} and D. Garcia-Gamez and S. Gardiner",

note = "Funding Information: This document was prepared by the MicroBooNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; the European Union{\textquoteright}s Horizon 2020 Marie Sklodowska-Curie Actions; and The Royal Society (United Kingdom). Additional support for the laser calibration system and cosmic ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland. Publisher Copyright: {\textcopyright} 2021 American Physical Society.",

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doi = "10.1103/PhysRevD.103.052012",

language = "English (US)",

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AU - (The MicroBooNE Collaboration)

AU - Abratenko, P.

AU - Alrashed, M.

AU - An, R.

AU - Anthony, J.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Balasubramanian, S.

AU - Baller, B.

AU - Barnes, C.

AU - Barr, G.

AU - Basque, V.

AU - Bathe-Peters, L.

AU - Benevides Rodrigues, O.

AU - Berkman, S.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Caro Terrazas, I.

AU - Castillo Fernandez, R.

AU - Cavanna, F.

AU - Cerati, G.

AU - Chen, Y.

AU - Church, E.

AU - Cianci, D.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadón, J. I.

AU - Del Tutto, M.

AU - Dennis, S. R.

AU - Devitt, D.

AU - Diurba, R.

AU - Dorrill, R.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Evans, J. J.

AU - Fiorentini Aguirre, G. A.

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Foppiani, N.

AU - Franco, D.

AU - Furmanski, A. P.

AU - Garcia-Gamez, D.

AU - Gardiner, S.

N1 - Funding Information: This document was prepared by the MicroBooNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; the European Union’s Horizon 2020 Marie Sklodowska-Curie Actions; and The Royal Society (United Kingdom). Additional support for the laser calibration system and cosmic ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland. Publisher Copyright: © 2021 American Physical Society.

PY - 2021/3/26

Y1 - 2021/3/26

N2 - We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥99%. For full neutrino interaction simulations, the time for processing one image is ≈0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

AB - We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥99%. For full neutrino interaction simulations, the time for processing one image is ≈0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

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Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

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