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Title: Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning

Abstract

NOvA is a long-baseline neutrino oscillation experiment which measures the oscillation of muon neutrinos from the NuMI beam at Fermilab after they travel through the Earth for 810 km. In this dissertation I describe the operations and monitoring of the detectors which make it possible to record over 98% of the delivered neutrino beam. I also present reconstruction and identification techniques using deep convolutional neural networks (CNNs), which are applicable to multiple analyses. Lastly, I detail the oscillation analyses in the $$\nu_{\mu} \to \nu_{\mu}$$ and $$\nu_{\mu} \to\nu_e$$ channels on a dataset of 8:85 x $$10^{20}$$ protons on target, with emphasis on the application of deep learning, energy reconstruction, and treatment of systematic uncertainties....

Authors:
 [1]
  1. Indiana U.
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)
OSTI Identifier:
1437288
Report Number(s):
FERMILAB-THESIS-2018-07
1672901
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English

Citation Formats

Psihas, Fernanda. Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning. United States: N. p., 2018. Web. doi:10.2172/1437288.
Psihas, Fernanda. Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning. United States. doi:10.2172/1437288.
Psihas, Fernanda. Mon . "Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning". United States. doi:10.2172/1437288. https://www.osti.gov/servlets/purl/1437288.
@article{osti_1437288,
title = {Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning},
author = {Psihas, Fernanda},
abstractNote = {NOvA is a long-baseline neutrino oscillation experiment which measures the oscillation of muon neutrinos from the NuMI beam at Fermilab after they travel through the Earth for 810 km. In this dissertation I describe the operations and monitoring of the detectors which make it possible to record over 98% of the delivered neutrino beam. I also present reconstruction and identification techniques using deep convolutional neural networks (CNNs), which are applicable to multiple analyses. Lastly, I detail the oscillation analyses in the $\nu_{\mu} \to \nu_{\mu}$ and $\nu_{\mu} \to\nu_e$ channels on a dataset of 8:85 x $10^{20}$ protons on target, with emphasis on the application of deep learning, energy reconstruction, and treatment of systematic uncertainties....},
doi = {10.2172/1437288},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}

Thesis/Dissertation:
Other availability
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