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Title: The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

Abstract

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

Authors:
 [1]
  1. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); et al.
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.:
[NOvA Collaboration]
OSTI Identifier:
1423233
Report Number(s):
[FERMILAB-CONF-16-737]
[Journal ID: ISSN 1742-6588; 1638573]
Grant/Contract Number:  
[AC02-07CH11359]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
[ Journal Volume: 898; Journal Issue: 7; Conference: 22nd International Conference on Computing in High Energy and Nuclear Physics, San Francisco, CA, 10/10-10/14/2016]; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Psihas, Fernanda. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events. United States: N. p., 2017. Web. doi:10.1088/1742-6596/898/7/072053.
Psihas, Fernanda. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events. United States. doi:10.1088/1742-6596/898/7/072053.
Psihas, Fernanda. Sun . "The Convolutional Visual Network for Identification and Reconstruction of NOvA Events". United States. doi:10.1088/1742-6596/898/7/072053. https://www.osti.gov/servlets/purl/1423233.
@article{osti_1423233,
title = {The Convolutional Visual Network for Identification and Reconstruction of NOvA Events},
author = {Psihas, Fernanda},
abstractNote = {In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.},
doi = {10.1088/1742-6596/898/7/072053},
journal = {Journal of Physics. Conference Series},
number = [7],
volume = [898],
place = {United States},
year = {2017},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: NOvA characteristic data events. Side views of 3x11 meter sections of the detector. The color of the hits indicates deposited charge (measured in ADC counts). The neutrino neutral current interactions (bottom), as well as the charged current interactions for electron (middle) and muon (top) flavor are each themore » main signal on NOvA's neutral current, νe appearance and νµ disappearance analyses, respectively. This makes the classification of these events the crucial first step for these analyses.« less

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.