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Cosmic Background Removal with Deep Neural Networks in SBND

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

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Cosmic Background Removal with Deep Neural Networks in SBND. / SBND Collaboration ; Brailsford, D.; Nowak, Jaroslaw et al.
In: Frontiers in Artificial Intelligence, Vol. 4, 649917, 24.08.2021.

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

Harvard

SBND Collaboration, Brailsford, D, Nowak, J & Lay, H 2021, 'Cosmic Background Removal with Deep Neural Networks in SBND', Frontiers in Artificial Intelligence, vol. 4, 649917. https://doi.org/10.3389/frai.2021.649917

APA

SBND Collaboration, Brailsford, D., Nowak, J., & Lay, H. (2021). Cosmic Background Removal with Deep Neural Networks in SBND. Frontiers in Artificial Intelligence, 4, Article 649917. https://doi.org/10.3389/frai.2021.649917

Vancouver

SBND Collaboration, Brailsford D, Nowak J, Lay H. Cosmic Background Removal with Deep Neural Networks in SBND. Frontiers in Artificial Intelligence. 2021 Aug 24;4:649917. doi: 10.3389/frai.2021.649917

Author

SBND Collaboration ; Brailsford, D. ; Nowak, Jaroslaw et al. / Cosmic Background Removal with Deep Neural Networks in SBND. In: Frontiers in Artificial Intelligence. 2021 ; Vol. 4.

Bibtex

@article{30a824b3037b49ecaba4d1aeb9c81398,
title = "Cosmic Background Removal with Deep Neural Networks in SBND",
abstract = "In this paper, we have demonstrated a novel technique for pixel level segmentation to remove cosmic backgrounds from LArTPC images. We have shown how different deep neural networks can be designed and trained for this task, and presented metrics that can be used to select the best versions. The technique developed is applicable to other LArTPC detectors running at surface level, such as MicroBooNE, ICARUS and ProtoDUNE. We anticipate future publications studying the hyperparameters of these networks, and an updated dataset with a more realistic detector simulation prior to the application of this technique to real neutrino data. ",
keywords = "physics.data-an",
author = "{SBND Collaboration} and R. Acciarri and C. Backhouse and W. Badgett and L. Bagby and V. Basque and M. and C. and Q. Bazetto and A. Bhanderi and A. Bhat and D. Brailsford and A. and G. Brandt and M. and F. Carneiro and G. Chisnall and J. and I. Crespo-Anad{\'o}n and E. Cristaldo and C. Cuesta and I. and Astiz, {L. de Icaza} and Roeck, {A. De} and Tutto, {M. Del} and Benedetto, {V. Di} and A. Ereditato and J. and A. and C. Ezeribe and R. and B. and T. Fleming and W. Foreman and D. Franco and S. Gao and D. Garcia-Gamez and H. Frandini and I. Gil-Botella and S. Gollapinni and O. Goodwin and W. and C. Griffith and R. Guenette and P. Guzowski and T. Ham and A. Holin and D. Kalra and L. Kashur and M. and Jaroslaw Nowak and Henry Lay",
year = "2021",
month = aug,
day = "24",
doi = "10.3389/frai.2021.649917",
language = "English",
volume = "4",
journal = "Frontiers in Artificial Intelligence",
issn = "2624-8212",
publisher = "Frontiers Media",

}

RIS

TY - JOUR

T1 - Cosmic Background Removal with Deep Neural Networks in SBND

AU - SBND Collaboration

AU - Acciarri, R.

AU - Backhouse, C.

AU - Badgett, W.

AU - Bagby, L.

AU - Basque, V.

AU - M.,

AU - C.,

AU - Bazetto, Q.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Brailsford, D.

AU - A.,

AU - Brandt, G.

AU - M.,

AU - Carneiro, F.

AU - Chisnall, G.

AU - J.,

AU - Crespo-Anadón, I.

AU - Cristaldo, E.

AU - Cuesta, C.

AU - I.,

AU - Astiz, L. de Icaza

AU - Roeck, A. De

AU - Tutto, M. Del

AU - Benedetto, V. Di

AU - Ereditato, A.

AU - J.,

AU - A.,

AU - Ezeribe, C.

AU - R.,

AU - B.,

AU - Fleming, T.

AU - Foreman, W.

AU - Franco, D.

AU - Gao, S.

AU - Garcia-Gamez, D.

AU - Frandini, H.

AU - Gil-Botella, I.

AU - Gollapinni, S.

AU - Goodwin, O.

AU - W.,

AU - Griffith, C.

AU - Guenette, R.

AU - Guzowski, P.

AU - Ham, T.

AU - Holin, A.

AU - Kalra, D.

AU - Kashur, L.

AU - M.,

AU - Nowak, Jaroslaw

AU - Lay, Henry

PY - 2021/8/24

Y1 - 2021/8/24

N2 - In this paper, we have demonstrated a novel technique for pixel level segmentation to remove cosmic backgrounds from LArTPC images. We have shown how different deep neural networks can be designed and trained for this task, and presented metrics that can be used to select the best versions. The technique developed is applicable to other LArTPC detectors running at surface level, such as MicroBooNE, ICARUS and ProtoDUNE. We anticipate future publications studying the hyperparameters of these networks, and an updated dataset with a more realistic detector simulation prior to the application of this technique to real neutrino data.

AB - In this paper, we have demonstrated a novel technique for pixel level segmentation to remove cosmic backgrounds from LArTPC images. We have shown how different deep neural networks can be designed and trained for this task, and presented metrics that can be used to select the best versions. The technique developed is applicable to other LArTPC detectors running at surface level, such as MicroBooNE, ICARUS and ProtoDUNE. We anticipate future publications studying the hyperparameters of these networks, and an updated dataset with a more realistic detector simulation prior to the application of this technique to real neutrino data.

KW - physics.data-an

U2 - 10.3389/frai.2021.649917

DO - 10.3389/frai.2021.649917

M3 - Journal article

VL - 4

JO - Frontiers in Artificial Intelligence

JF - Frontiers in Artificial Intelligence

SN - 2624-8212

M1 - 649917

ER -