Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber

Physics

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Experimental Particle Physics

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1808.07269v1
Rights statement: © 2019 American Physical Society
Accepted author manuscript, 3.39 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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https://doi.org/10.1103/PhysRevD.99.092001
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Available under license: CC BY: Creative Commons Attribution 4.0 International License

Keywords

physics.ins-det, cs.CV, hep-ex, physics.data-an

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. / Adams, C.; Blake, A.; Devitt, D. et al.
In: Physical Review D, Vol. 99, No. 9, 092001, 07.05.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{7ea2267a427144f3a3d8529064836628,

title = "Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber",

abstract = "We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a nu(mu) charged-current neutral pion data samples.",

keywords = "physics.ins-det, cs.CV, hep-ex, physics.data-an",

author = "C. Adams and M. Alrashed and R. An and J. Anthony and J. Asaadi and A. Ashkenazi and M. Auger and S. Balasubramanian and B. Baller and C. Barnes and G. Barr and M. Bass and F. Bay and A. Bhat and K. Bhattacharya and M. Bishai and A. Blake and T. Bolton and L. Camilleri and D. Caratelli and Terrazas, {I. Caro} and Fernandez, {R. Castillo} and F. Cavanna and G. Cerati and E. Church and D. Cianci and E. Cohen and Collin, {G. H.} and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anadon, {J. I.} and Tutto, {M. Del} and D. Devitt and A. Diaz and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and Sanchez, {L. Escudero} and J. Esquivel and Fadeeva, {A. A.} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and D. Franco and Furmanski, {A. P.} and D. Garcia-Gamez and Garvey, {G. T.} and A. Lister and J. Nowak and {MicroBooNE collaboration}",

note = "{\textcopyright} 2019 American Physical Society",

year = "2019",

month = may,

day = "7",

doi = "10.1103/PhysRevD.99.092001",

language = "English",

volume = "99",

journal = "Physical Review D",

issn = "1550-7998",

publisher = "American Physical Society",

number = "9",

}

RIS

TY - JOUR

T1 - Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber

AU - Adams, C.

AU - Alrashed, M.

AU - An, R.

AU - Anthony, J.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Auger, M.

AU - Balasubramanian, S.

AU - Baller, B.

AU - Barnes, C.

AU - Barr, G.

AU - Bass, M.

AU - Bay, F.

AU - Bhat, A.

AU - Bhattacharya, K.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Terrazas, I. Caro

AU - Fernandez, R. Castillo

AU - Cavanna, F.

AU - Cerati, G.

AU - Church, E.

AU - Cianci, D.

AU - Cohen, E.

AU - Collin, G. H.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadon, J. I.

AU - Tutto, M. Del

AU - Devitt, D.

AU - Diaz, A.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Sanchez, L. Escudero

AU - Esquivel, J.

AU - Fadeeva, A. A.

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Franco, D.

AU - Furmanski, A. P.

AU - Garcia-Gamez, D.

AU - Garvey, G. T.

AU - Lister, A.

AU - Nowak, J.

AU - MicroBooNE collaboration

PY - 2019/5/7

Y1 - 2019/5/7

N2 - We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a nu(mu) charged-current neutral pion data samples.

AB - We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a nu(mu) charged-current neutral pion data samples.

KW - physics.ins-det

KW - cs.CV

KW - hep-ex

KW - physics.data-an

U2 - 10.1103/PhysRevD.99.092001

DO - 10.1103/PhysRevD.99.092001

M3 - Journal article

VL - 99

JO - Physical Review D

JF - Physical Review D

SN - 1550-7998

IS - 9

M1 - 092001

ER -

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