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First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE

Research output: Working paper › Preprint

Published

Standard

First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. / MicroBooNE Collaboration.
2018.

Research output: Working paper › Preprint

Harvard

MicroBooNE Collaboration 2018 'First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE'. https://doi.org/10.2172/1573220

APA

MicroBooNE Collaboration (2018). First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. https://doi.org/10.2172/1573220

Vancouver

MicroBooNE Collaboration. First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. 2018 Jul 9. doi: 10.2172/1573220

Author

MicroBooNE Collaboration. / First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. 2018.

Bibtex

@techreport{64275e24464b4880af14853774763c90,
title = "First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE",
abstract = "This paper describes algorithms developed to isolate and accurately reconstruct two-track νµ-like events that are contained within the MicroBooNE detector. This reconstruction has applications to searches for neutrino oscillations and measurements of cross sections using events that are chargedcurrent quasi-elastic-like, among other applications. The algorithms we discuss will be applicable to all detectors running in Fermilab{\textquoteright}s SBN program, and any future LArTPC experiment with beam energies ∼ 1 GeV",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2018",
month = jul,
day = "9",
doi = "10.2172/1573220",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2018/7/9

Y1 - 2018/7/9

N2 - This paper describes algorithms developed to isolate and accurately reconstruct two-track νµ-like events that are contained within the MicroBooNE detector. This reconstruction has applications to searches for neutrino oscillations and measurements of cross sections using events that are chargedcurrent quasi-elastic-like, among other applications. The algorithms we discuss will be applicable to all detectors running in Fermilab’s SBN program, and any future LArTPC experiment with beam energies ∼ 1 GeV

AB - This paper describes algorithms developed to isolate and accurately reconstruct two-track νµ-like events that are contained within the MicroBooNE detector. This reconstruction has applications to searches for neutrino oscillations and measurements of cross sections using events that are chargedcurrent quasi-elastic-like, among other applications. The algorithms we discuss will be applicable to all detectors running in Fermilab’s SBN program, and any future LArTPC experiment with beam energies ∼ 1 GeV

U2 - 10.2172/1573220

DO - 10.2172/1573220

M3 - Preprint

BT - First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE

ER -