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A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

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

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A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber. / MicroBooNE Collaboration ; Blake, A.; Devitt, D. et al.
In: Physical Review D, Vol. 103, No. 9, 092003, 01.05.2021.

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

Harvard

MicroBooNE Collaboration, Blake, A, Devitt, D, Nowak, J & Thorpe, C 2021, 'A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber', Physical Review D, vol. 103, no. 9, 092003. https://doi.org/10.1103/PhysRevD.103.092003

APA

MicroBooNE Collaboration, Blake, A., Devitt, D., Nowak, J., & Thorpe, C. (2021). A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber. Physical Review D, 103(9), Article 092003. https://doi.org/10.1103/PhysRevD.103.092003

Vancouver

MicroBooNE Collaboration, Blake A, Devitt D, Nowak J, Thorpe C. A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber. Physical Review D. 2021 May 1;103(9):092003. doi: 10.1103/PhysRevD.103.092003

Author

MicroBooNE Collaboration ; Blake, A. ; Devitt, D. et al. / A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber. In: Physical Review D. 2021 ; Vol. 103, No. 9.

Bibtex

@article{3bce783a9471487eb422f5b911607d93,
title = "A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber",
abstract = " We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based $\nu_e$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector. ",
keywords = "hep-ex",
author = "{MicroBooNE Collaboration} and P. Abratenko and M. Alrashed and R. An and J. Anthony 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 L. Camilleri and D. Caratelli and Terrazas, {I. Caro} and Fernandez, {R. Castillo} and F. Cavanna and G. Cerati and E. Church and D. Cianci and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anadon, {J. I.} and Tutto, {M. Del} and S. Dennis and D. Devitt and R. Diurba and L. Domine and R. Dorrill and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and Sanchez, {L. Escudero} and Aguirre, {G. A. Fiorentini} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and N. Foppiani and D. Franco and Furmanski, {A. P.} and J. Nowak and C. Thorpe",
year = "2021",
month = may,
day = "1",
doi = "10.1103/PhysRevD.103.092003",
language = "English",
volume = "103",
journal = "Physical Review D",
issn = "1550-7998",
publisher = "American Physical Society",
number = "9",

}

RIS

TY - JOUR

T1 - A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

AU - MicroBooNE Collaboration

AU - Abratenko, P.

AU - Alrashed, M.

AU - An, R.

AU - Anthony, J.

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 - Camilleri, L.

AU - Caratelli, D.

AU - Terrazas, I. Caro

AU - Fernandez, R. Castillo

AU - Cavanna, F.

AU - Cerati, G.

AU - Church, E.

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.

AU - Devitt, D.

AU - Diurba, R.

AU - Domine, L.

AU - Dorrill, R.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Sanchez, L. Escudero

AU - Aguirre, G. A. Fiorentini

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Foppiani, N.

AU - Franco, D.

AU - Furmanski, A. P.

AU - Nowak, J.

AU - Thorpe, C.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based $\nu_e$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.

AB - We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based $\nu_e$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.

KW - hep-ex

U2 - 10.1103/PhysRevD.103.092003

DO - 10.1103/PhysRevD.103.092003

M3 - Journal article

VL - 103

JO - Physical Review D

JF - Physical Review D

SN - 1550-7998

IS - 9

M1 - 092003

ER -