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Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis

Research output: Working paper › Preprint

Published

Standard

Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis. / MicroBooNE Collaboration.
2018.

Research output: Working paper › Preprint

Harvard

MicroBooNE Collaboration 2018 'Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis'. https://doi.org/10.2172/1573224

APA

MicroBooNE Collaboration (2018). Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis. https://doi.org/10.2172/1573224

Vancouver

MicroBooNE Collaboration. Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis. 2018 Oct 29. doi: 10.2172/1573224

Author

MicroBooNE Collaboration. / Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis. 2018.

Bibtex

@techreport{4a01ee113c3a4b079dff6bebd5a3733a,
title = "Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis",
abstract = "MicroBooNE is a large liquid argon time projection chamber (LArTPC) on Fermilab{\textquoteright}s Booster Neutrino Beam (BNB). A main goal of MicroBooNE is to search for the low energy excess (LEE) of electron like events seen by MiniBooNE. Using νµ interactions to constrain νe systematics is a common approach in oscillation experiments, we will adopt it here as well. This takes adantage of the high statistics νµ data and known correlations between electron and muon neutrino fluxes and cross sections. This note provides an overview of the selection of events with one reconstructed muon and one reconstructed proton in (µ1p) in MicroBooNE using a Deep Learning based reconstruction. We then present comparisons between data on our simulated neutrino interaction predictions for some important kinematic distributions. We find that in a sample of data corresponding to 4× 1019 POT, that the data and simulation agree well in shape.",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2018",
month = oct,
day = "29",
doi = "10.2172/1573224",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2018/10/29

Y1 - 2018/10/29

N2 - MicroBooNE is a large liquid argon time projection chamber (LArTPC) on Fermilab’s Booster Neutrino Beam (BNB). A main goal of MicroBooNE is to search for the low energy excess (LEE) of electron like events seen by MiniBooNE. Using νµ interactions to constrain νe systematics is a common approach in oscillation experiments, we will adopt it here as well. This takes adantage of the high statistics νµ data and known correlations between electron and muon neutrino fluxes and cross sections. This note provides an overview of the selection of events with one reconstructed muon and one reconstructed proton in (µ1p) in MicroBooNE using a Deep Learning based reconstruction. We then present comparisons between data on our simulated neutrino interaction predictions for some important kinematic distributions. We find that in a sample of data corresponding to 4× 1019 POT, that the data and simulation agree well in shape.

AB - MicroBooNE is a large liquid argon time projection chamber (LArTPC) on Fermilab’s Booster Neutrino Beam (BNB). A main goal of MicroBooNE is to search for the low energy excess (LEE) of electron like events seen by MiniBooNE. Using νµ interactions to constrain νe systematics is a common approach in oscillation experiments, we will adopt it here as well. This takes adantage of the high statistics νµ data and known correlations between electron and muon neutrino fluxes and cross sections. This note provides an overview of the selection of events with one reconstructed muon and one reconstructed proton in (µ1p) in MicroBooNE using a Deep Learning based reconstruction. We then present comparisons between data on our simulated neutrino interaction predictions for some important kinematic distributions. We find that in a sample of data corresponding to 4× 1019 POT, that the data and simulation agree well in shape.

U2 - 10.2172/1573224

DO - 10.2172/1573224

M3 - Preprint

BT - Selection of numu Events for the MicroBooNE Deep Learning Low Energy Excess Analysis

ER -