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Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search

Research output: Working paper › Preprint

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Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search. / MicroBooNE Collaboration.
2020.

Research output: Working paper › Preprint

Harvard

MicroBooNE Collaboration 2020 'Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search'. https://doi.org/10.2172/2397313

APA

MicroBooNE Collaboration (2020). Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search. https://doi.org/10.2172/2397313

Vancouver

MicroBooNE Collaboration. Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search. 2020 Jun 23. doi: 10.2172/2397313

Author

MicroBooNE Collaboration. / Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search. 2020.

Bibtex

@techreport{437f7de0512845ccaa758e749c395890,
title = "Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search",
abstract = "MicroBooNE is a short baseline neutrino oscillation experiment based at Fermilab that employs Liquid Argon Time Projection Chamber (LArTPC) technology. One of its target measurements is to investigate the nature of the excess of low energy electron-like events observed by MiniBooNE. This measurement will require an excellent understanding of systematic uncertainties, obtained through testing the performance of reconstruction algorithms on samples with known properties. However, using exclusively Monte Carlo events for this task is limited by how well the discrepancies between simulation and data are understood. An alternative is to test against samples of “chimera” events, which are made up of separate single-particle components from data that are combined to create neutrino-like events. These chimera events can be used to help quantify systematic uncertainties. This note covers the performance and status of creating and using chimera events that match a target neutrino topology in MicroBooNE.",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2020",
month = jun,
day = "23",
doi = "10.2172/2397313",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2020/6/23

Y1 - 2020/6/23

N2 - MicroBooNE is a short baseline neutrino oscillation experiment based at Fermilab that employs Liquid Argon Time Projection Chamber (LArTPC) technology. One of its target measurements is to investigate the nature of the excess of low energy electron-like events observed by MiniBooNE. This measurement will require an excellent understanding of systematic uncertainties, obtained through testing the performance of reconstruction algorithms on samples with known properties. However, using exclusively Monte Carlo events for this task is limited by how well the discrepancies between simulation and data are understood. An alternative is to test against samples of “chimera” events, which are made up of separate single-particle components from data that are combined to create neutrino-like events. These chimera events can be used to help quantify systematic uncertainties. This note covers the performance and status of creating and using chimera events that match a target neutrino topology in MicroBooNE.

AB - MicroBooNE is a short baseline neutrino oscillation experiment based at Fermilab that employs Liquid Argon Time Projection Chamber (LArTPC) technology. One of its target measurements is to investigate the nature of the excess of low energy electron-like events observed by MiniBooNE. This measurement will require an excellent understanding of systematic uncertainties, obtained through testing the performance of reconstruction algorithms on samples with known properties. However, using exclusively Monte Carlo events for this task is limited by how well the discrepancies between simulation and data are understood. An alternative is to test against samples of “chimera” events, which are made up of separate single-particle components from data that are combined to create neutrino-like events. These chimera events can be used to help quantify systematic uncertainties. This note covers the performance and status of creating and using chimera events that match a target neutrino topology in MicroBooNE.

U2 - 10.2172/2397313

DO - 10.2172/2397313

M3 - Preprint

BT - Chimera Events for Performance Studies of the MicroBooNE Deep Learning-based Low Energy Excess Search

ER -