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Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks

Research output: Working paper › Preprint

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Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks. / MicroBooNE Collaboration.
2020.

Research output: Working paper › Preprint

Harvard

MicroBooNE Collaboration 2020 'Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks'. https://doi.org/10.2172/2397314

APA

MicroBooNE Collaboration (2020). Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks. https://doi.org/10.2172/2397314

Vancouver

MicroBooNE Collaboration. Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks. 2020 Jun 23. doi: 10.2172/2397314

Author

MicroBooNE Collaboration. / Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks. 2020.

Bibtex

@techreport{b4fc7854b1064209ad9cd5447c3f5365,
title = "Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks",
abstract = "We have developed a convolutional neural network (CNN) that reconstructs the three dimensional location of ionization produced by charge particles traversing a wire-readout liquid argon time projection chamber (LArTPC) at MicroBooNE. This method is the first CNN application to reconstruct 3D space points directly from two dimensional images of the different wire plane views. I.",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2020",
month = jun,
day = "23",
doi = "10.2172/2397314",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2020/6/23

Y1 - 2020/6/23

N2 - We have developed a convolutional neural network (CNN) that reconstructs the three dimensional location of ionization produced by charge particles traversing a wire-readout liquid argon time projection chamber (LArTPC) at MicroBooNE. This method is the first CNN application to reconstruct 3D space points directly from two dimensional images of the different wire plane views. I.

AB - We have developed a convolutional neural network (CNN) that reconstructs the three dimensional location of ionization produced by charge particles traversing a wire-readout liquid argon time projection chamber (LArTPC) at MicroBooNE. This method is the first CNN application to reconstruct 3D space points directly from two dimensional images of the different wire plane views. I.

U2 - 10.2172/2397314

DO - 10.2172/2397314

M3 - Preprint

BT - Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks

ER -