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    Rights statement: This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/12/03/P03011

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Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

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

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Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. / MicroBooNE Collaboration.
In: Journal of Instrumentation, Vol. 12, P03011, 14.03.2017.

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

Harvard

MicroBooNE Collaboration 2017, 'Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber', Journal of Instrumentation, vol. 12, P03011. https://doi.org/10.1088/1748-0221/12/03/P03011

APA

MicroBooNE Collaboration (2017). Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. Journal of Instrumentation, 12, Article P03011. https://doi.org/10.1088/1748-0221/12/03/P03011

Vancouver

MicroBooNE Collaboration. Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. Journal of Instrumentation. 2017 Mar 14;12:P03011. doi: 10.1088/1748-0221/12/03/P03011

Author

MicroBooNE Collaboration. / Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. In: Journal of Instrumentation. 2017 ; Vol. 12.

Bibtex

@article{4ee956818c774d009c88e5b726c4b6be,
title = "Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber",
abstract = "We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.",
keywords = "physics.ins-det, hep-ex",
author = "R. Acciarri and R. An and J. Asaadi and M. Auger and L. Bagby and B. Baller and G. Barr and M. Bass and F. Bay and M. Bishai and A. Blake and T. Bolton and L. Bugel and L. Camilleri and D. Caratelli and B. Carls and Fernandez, {R. Castillo} and F. Cavanna and E. Church and D. Cianci and Collin, {G. H.} and Conrad, {J. M.} and M. Convery and Crespo-Anad{\'o}n, {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 Fleming, {B. T.} and W. Foreman and Furmanski, {A. P.} 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 J. Ho and A. Lister and J. Nowak and {MicroBooNE Collaboration}",
note = "This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/12/03/P03011",
year = "2017",
month = mar,
day = "14",
doi = "10.1088/1748-0221/12/03/P03011",
language = "English",
volume = "12",
journal = "Journal of Instrumentation",
issn = "1748-0221",
publisher = "Institute of Physics Publishing",

}

RIS

TY - JOUR

T1 - Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

AU - Acciarri, R.

AU - An, R.

AU - Asaadi, J.

AU - Auger, M.

AU - Bagby, L.

AU - Baller, B.

AU - Barr, G.

AU - Bass, M.

AU - Bay, F.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Bugel, L.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Carls, B.

AU - Fernandez, R. Castillo

AU - Cavanna, F.

AU - Church, E.

AU - Cianci, D.

AU - Collin, G. H.

AU - Conrad, J. M.

AU - Convery, M.

AU - Crespo-Anadón, 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 - Fleming, B. T.

AU - Foreman, W.

AU - Furmanski, A. P.

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 - Ho, J.

AU - Lister, A.

AU - Nowak, J.

AU - MicroBooNE Collaboration

N1 - This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/12/03/P03011

PY - 2017/3/14

Y1 - 2017/3/14

N2 - We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

AB - We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

KW - physics.ins-det

KW - hep-ex

U2 - 10.1088/1748-0221/12/03/P03011

DO - 10.1088/1748-0221/12/03/P03011

M3 - Journal article

VL - 12

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

M1 - P03011

ER -