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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. / DUNE Collaboration ; Blake, A.; Brailsford, D. et al.
In: European Physical Journal C: Particles and Fields, Vol. 82, 903, 12.10.2022.

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

Harvard

DUNE Collaboration, Blake, A, Brailsford, D, Cross, R, Mouster, G, Nowak, JA & Ratoff, P 2022, 'Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network', European Physical Journal C: Particles and Fields, vol. 82, 903. https://doi.org/10.1140/epjc/s10052-022-10791-2

APA

DUNE Collaboration, Blake, A., Brailsford, D., Cross, R., Mouster, G., Nowak, J. A., & Ratoff, P. (2022). Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. European Physical Journal C: Particles and Fields, 82, Article 903. https://doi.org/10.1140/epjc/s10052-022-10791-2

Vancouver

DUNE Collaboration, Blake A, Brailsford D, Cross R, Mouster G, Nowak JA et al. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. European Physical Journal C: Particles and Fields. 2022 Oct 12;82:903. doi: 10.1140/epjc/s10052-022-10791-2

Author

DUNE Collaboration ; Blake, A. ; Brailsford, D. et al. / Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. In: European Physical Journal C: Particles and Fields. 2022 ; Vol. 82.

Bibtex

@article{ba72ed2ef2d745a7ac25611029ccd9cb,
title = "Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network",
abstract = " Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation. ",
keywords = "physics.ins-det, hep-ex",
author = "{DUNE Collaboration} and DUNE Collaboration and Abud, {A. Abed} and B. Abi and R. Acciarri and Acero, {M. A.} and Adames, {M. R.} and G. Adamov and M. Adamowski and D. Adams and M. Adinolfi and A. Aduszkiewicz and J. Aguilar and Z. Ahmad and J. Ahmed and B. Aimard and B. Ali-Mohammadzadeh and T. Alion and K. Allison and Monsalve, {S. Alonso} and M. AlRashed and C. Alt and A. Alton and R. Alvarez and P. Amedo and C. Andreopoulos and M. Andreotti and M. Andrews and F. Andrianala and S. Andringa and N. Anfimov and A. Ankowski and M. Antoniassi and M. Antonova and A. Antoshkin and S. Antusch and A. Aranda-Fernandez and L. Arellano and Arnold, {L. O.} and Arroyave, {M. A.} and J. Asaadi and L. Asquith and A. Aurisano and V. Aushev and D. Autiero and A. Blake and D. Brailsford and R. Cross and G. Mouster and Nowak, {J. A.} and P. Ratoff",
note = "31 pages, 15 figures",
year = "2022",
month = oct,
day = "12",
doi = "10.1140/epjc/s10052-022-10791-2",
language = "English",
volume = "82",
journal = "European Physical Journal C: Particles and Fields",
issn = "1434-6044",
publisher = "SPRINGER",

}

RIS

TY - JOUR

T1 - Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

AU - DUNE Collaboration

AU - Collaboration, DUNE

AU - Abud, A. Abed

AU - Abi, B.

AU - Acciarri, R.

AU - Acero, M. A.

AU - Adames, M. R.

AU - Adamov, G.

AU - Adamowski, M.

AU - Adams, D.

AU - Adinolfi, M.

AU - Aduszkiewicz, A.

AU - Aguilar, J.

AU - Ahmad, Z.

AU - Ahmed, J.

AU - Aimard, B.

AU - Ali-Mohammadzadeh, B.

AU - Alion, T.

AU - Allison, K.

AU - Monsalve, S. Alonso

AU - AlRashed, M.

AU - Alt, C.

AU - Alton, A.

AU - Alvarez, R.

AU - Amedo, P.

AU - Andreopoulos, C.

AU - Andreotti, M.

AU - Andrews, M.

AU - Andrianala, F.

AU - Andringa, S.

AU - Anfimov, N.

AU - Ankowski, A.

AU - Antoniassi, M.

AU - Antonova, M.

AU - Antoshkin, A.

AU - Antusch, S.

AU - Aranda-Fernandez, A.

AU - Arellano, L.

AU - Arnold, L. O.

AU - Arroyave, M. A.

AU - Asaadi, J.

AU - Asquith, L.

AU - Aurisano, A.

AU - Aushev, V.

AU - Autiero, D.

AU - Blake, A.

AU - Brailsford, D.

AU - Cross, R.

AU - Mouster, G.

AU - Nowak, J. A.

AU - Ratoff, P.

N1 - 31 pages, 15 figures

PY - 2022/10/12

Y1 - 2022/10/12

N2 - Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.

AB - Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.

KW - physics.ins-det

KW - hep-ex

U2 - 10.1140/epjc/s10052-022-10791-2

DO - 10.1140/epjc/s10052-022-10791-2

M3 - Journal article

VL - 82

JO - European Physical Journal C: Particles and Fields

JF - European Physical Journal C: Particles and Fields

SN - 1434-6044

M1 - 903

ER -