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  • Shower_Energy_Validation_with_Michel___Pi0_Samples_in_MicroBooNE__Paper_ (1)

    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:10.1088/1748-0221/16/12/T12017

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Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE

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

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Standard

Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE. / MicroBooNE Collaboration ; Blake, A.; Devitt, A. et al.
In: Journal of Instrumentation, Vol. 16, T12017, 22.12.2021.

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

Harvard

MicroBooNE Collaboration, Blake, A, Devitt, A, Nowak, J, Patel, N & Thorpe, C 2021, 'Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE', Journal of Instrumentation, vol. 16, T12017. https://doi.org/10.1088/1748-0221/16/12/T12017

APA

MicroBooNE Collaboration, Blake, A., Devitt, A., Nowak, J., Patel, N., & Thorpe, C. (2021). Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE. Journal of Instrumentation, 16, Article T12017. https://doi.org/10.1088/1748-0221/16/12/T12017

Vancouver

MicroBooNE Collaboration, Blake A, Devitt A, Nowak J, Patel N, Thorpe C. Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE. Journal of Instrumentation. 2021 Dec 22;16:T12017. doi: 10.1088/1748-0221/16/12/T12017

Author

MicroBooNE Collaboration ; Blake, A. ; Devitt, A. et al. / Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE. In: Journal of Instrumentation. 2021 ; Vol. 16.

Bibtex

@article{a7d4a5e7d51944df87b38dd66a9ca8ea,
title = "Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE",
abstract = " This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two $\nu_{\mu}$-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff. ",
keywords = "hep-ex",
author = "{MicroBooNE Collaboration} and P. Abratenko and R. An and J. Anthony and L. Arellano 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 Rodrigues, {O. Benevides} and S. Berkman and A. Bhanderi and A. Bhat and M. Bishai and A. Blake and T. Bolton and Book, {J. Y.} and L. Camilleri and D. Caratelli and Terrazas, {I. Caro} and F. Cavanna and G. Cerati and Y. Chen and D. Cianci and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anadon, {J. I.} and Tutto, {M. Del} and Dennis, {S. R.} and P. Detje and A. Devitt and R. Diurba and R. Dorrill and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and Evans, {J. J.} and R. Fine and Aguirre, {G. A. Fiorentini} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and A. Devitt and J. Nowak and N. Patel and C. Thorpe",
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:10.1088/1748-0221/16/12/T12017",
year = "2021",
month = dec,
day = "22",
doi = "10.1088/1748-0221/16/12/T12017",
language = "English",
volume = "16",
journal = "Journal of Instrumentation",
issn = "1748-0221",
publisher = "Institute of Physics Publishing",

}

RIS

TY - JOUR

T1 - Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE

AU - MicroBooNE Collaboration

AU - Abratenko, P.

AU - An, R.

AU - Anthony, J.

AU - Arellano, L.

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 - Rodrigues, O. Benevides

AU - Berkman, S.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Book, J. Y.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Terrazas, I. Caro

AU - Cavanna, F.

AU - Cerati, G.

AU - Chen, Y.

AU - Cianci, D.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadon, J. I.

AU - Tutto, M. Del

AU - Dennis, S. R.

AU - Detje, P.

AU - Devitt, A.

AU - Diurba, R.

AU - Dorrill, R.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Evans, J. J.

AU - Fine, R.

AU - Aguirre, G. A. Fiorentini

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Devitt, A.

AU - Nowak, J.

AU - Patel, N.

AU - Thorpe, C.

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:10.1088/1748-0221/16/12/T12017

PY - 2021/12/22

Y1 - 2021/12/22

N2 - This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two $\nu_{\mu}$-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.

AB - This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two $\nu_{\mu}$-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.

KW - hep-ex

U2 - 10.1088/1748-0221/16/12/T12017

DO - 10.1088/1748-0221/16/12/T12017

M3 - Journal article

VL - 16

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

M1 - T12017

ER -