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  • Wire_Cell_Pattern_Recognition_2.0.0

    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:10.1088/1748-0221/17/01/P01037

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Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

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

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Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation. / MicroBooNE Collaboration ; Blake, A.; Devitt, Alesha et al.
In: Journal of Instrumentation, Vol. 17, No. 1, PO1037, 31.01.2022.

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

Harvard

MicroBooNE Collaboration, Blake, A, Devitt, A, Nowak, J, Patel, N & Thorpe, C 2022, 'Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation', Journal of Instrumentation, vol. 17, no. 1, PO1037. https://doi.org/10.1088/1748-0221/17/01/P01037

APA

MicroBooNE Collaboration, Blake, A., Devitt, A., Nowak, J., Patel, N., & Thorpe, C. (2022). Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation. Journal of Instrumentation, 17(1), Article PO1037. https://doi.org/10.1088/1748-0221/17/01/P01037

Vancouver

MicroBooNE Collaboration, Blake A, Devitt A, Nowak J, Patel N, Thorpe C. Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation. Journal of Instrumentation. 2022 Jan 31;17(1):PO1037. Epub 2022 Jan 27. doi: 10.1088/1748-0221/17/01/P01037

Author

MicroBooNE Collaboration ; Blake, A. ; Devitt, Alesha et al. / Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs : Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation. In: Journal of Instrumentation. 2022 ; Vol. 17, No. 1.

Bibtex

@article{0f455df8c666405eb55d5be6402af0ea,
title = "Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation",
abstract = " Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and $dQ/dx$ (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$ interactions. This pattern recognition achieves 80-90\% reconstruction efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for charged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20\% energy reconstruction resolutions for charged-current neutrino interactions. ",
keywords = "hep-ex",
author = "{MicroBooNE Collaboration} and 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 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 Fernandez, {R. Castillo} and F. Cavanna and G. Cerati 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 R. Fine and Aguirre, {G. A. Fiorentini} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and N. Foppiani and D. Franco and Alesha 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:10.1088/1748-0221/17/01/P01037",
year = "2022",
month = jan,
day = "31",
doi = "10.1088/1748-0221/17/01/P01037",
language = "English",
volume = "17",
journal = "Journal of Instrumentation",
issn = "1748-0221",
publisher = "Institute of Physics Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs

T2 - Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

AU - MicroBooNE Collaboration

AU - collaboration, MicroBooNE

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 - 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 - Fernandez, R. Castillo

AU - Cavanna, F.

AU - Cerati, G.

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 - Fine, R.

AU - Aguirre, G. A. Fiorentini

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Foppiani, N.

AU - Franco, D.

AU - Devitt, Alesha

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:10.1088/1748-0221/17/01/P01037

PY - 2022/1/31

Y1 - 2022/1/31

N2 - Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and $dQ/dx$ (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$ interactions. This pattern recognition achieves 80-90\% reconstruction efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for charged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20\% energy reconstruction resolutions for charged-current neutrino interactions.

AB - Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and $dQ/dx$ (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$ interactions. This pattern recognition achieves 80-90\% reconstruction efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for charged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20\% energy reconstruction resolutions for charged-current neutrino interactions.

KW - hep-ex

U2 - 10.1088/1748-0221/17/01/P01037

DO - 10.1088/1748-0221/17/01/P01037

M3 - Journal article

VL - 17

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

IS - 1

M1 - PO1037

ER -