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Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition

Research output: Working paper › Preprint

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Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition. / MicroBooNE Collaboration.
2018.

Research output: Working paper › Preprint

Harvard

MicroBooNE Collaboration 2018 'Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition'. https://doi.org/10.2172/1573219

APA

MicroBooNE Collaboration (2018). Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition. https://doi.org/10.2172/1573219

Vancouver

MicroBooNE Collaboration. Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition. 2018 Jul 9. doi: 10.2172/1573219

Author

MicroBooNE Collaboration. / Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition. 2018.

Bibtex

@techreport{c35aab54f52240e9bafbca385a1c46f6,
title = "Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition",
abstract = "MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. In this note we present a fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state (νe CC0π-Np). The efficiency of the current selection algorithm is (46.5±0.3) %. We also show some cuts on kinematic and geometric variables which reject background events. These cuts have been validated by analyzing two event samples orthogonal to our signal. Future improvements have been identified which will improve the reconstruction efficiency, especially at low energy.",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2018",
month = jul,
day = "9",
doi = "10.2172/1573219",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2018/7/9

Y1 - 2018/7/9

N2 - MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. In this note we present a fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state (νe CC0π-Np). The efficiency of the current selection algorithm is (46.5±0.3) %. We also show some cuts on kinematic and geometric variables which reject background events. These cuts have been validated by analyzing two event samples orthogonal to our signal. Future improvements have been identified which will improve the reconstruction efficiency, especially at low energy.

AB - MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. In this note we present a fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state (νe CC0π-Np). The efficiency of the current selection algorithm is (46.5±0.3) %. We also show some cuts on kinematic and geometric variables which reject background events. These cuts have been validated by analyzing two event samples orthogonal to our signal. Future improvements have been identified which will improve the reconstruction efficiency, especially at low energy.

U2 - 10.2172/1573219

DO - 10.2172/1573219

M3 - Preprint

BT - Electron-neutrino selection and reconstruction in the MicroBooNE LArTPC using the Pandora multi-algorithm pattern recognition

ER -