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Title: Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE

Abstract

MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.

Authors:
 [1]
  1. MIT
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
MicroBooNE
OSTI Identifier:
1462053
Report Number(s):
FERMILAB-POSTER-18-083-ND
1683908
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Yates, L. Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE. United States: N. p., 2018. Web.
Yates, L. Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE. United States.
Yates, L. Mon . "Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE". United States. https://www.osti.gov/servlets/purl/1462053.
@article{osti_1462053,
title = {Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE},
author = {Yates, L.},
abstractNote = {MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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