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Title: Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network

Abstract

The NOvA experiment is a long baseline neutrino oscillation experiment with the objective to measure the oscillation probability of muon type neutrinos ($$\nu_{\mu}$$) into electron type neutrinos ($$\nu_e$$). NOvA measures the interactions of neutrinos from the NuMI beam in two functionally identical liquid scintillator detectors. The far detector detects the appearance of electron neutrinos, leading to measurement of the oscillation parameters under study. Using an off -axis beam with an 810 km baseline length, NOvA is sensitive to measuring the neutrino mass hierarchy, the CP violating parameter, and the octant of the mixing angle, $$\theta_{23}$$. The data presented in this thesis has been collected from October 2013 until May 2018. The first NOvA $$\nu_e$$ charged current identifier utilized an artificial neural network with the physical features of the highest energy reconstructed shower as inputs. The $$\nu_ e$$ charged current identifier in this thesis utilizes a Long Short-Term Memory network with the physical features of every reconstructed shower in a particular interaction. In addition to the Long Short-Term Memory network, there are two Boosted Decision Trees to assist in event level selection. In the analysis of the data, 54 $$\nu_e$$ candidate events were detected with an expected background of 15 events. The results of this analysis prefer the normal mass hierarchy with maximal mixing and $$\frac{\delta_{CP}}{\pi}=1.92_{-1.19}^{0.08}$$. Results from this analysis differ from the published NOvA analysis, due to the differences of electron identification techniques.

Authors:
ORCiD logo [1]
  1. Minnesota 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:
1529330
Report Number(s):
FERMILAB-THESIS-2018-34
1740455
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Vold, Andrew. Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network. United States: N. p., 2018. Web. doi:10.2172/1529330.
Vold, Andrew. Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network. United States. doi:10.2172/1529330.
Vold, Andrew. Mon . "Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network". United States. doi:10.2172/1529330. https://www.osti.gov/servlets/purl/1529330.
@article{osti_1529330,
title = {Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network},
author = {Vold, Andrew},
abstractNote = {The NOvA experiment is a long baseline neutrino oscillation experiment with the objective to measure the oscillation probability of muon type neutrinos ($\nu_{\mu}$) into electron type neutrinos ($\nu_e$). NOvA measures the interactions of neutrinos from the NuMI beam in two functionally identical liquid scintillator detectors. The far detector detects the appearance of electron neutrinos, leading to measurement of the oscillation parameters under study. Using an off -axis beam with an 810 km baseline length, NOvA is sensitive to measuring the neutrino mass hierarchy, the CP violating parameter, and the octant of the mixing angle, $\theta_{23}$. The data presented in this thesis has been collected from October 2013 until May 2018. The first NOvA $\nu_e$ charged current identifier utilized an artificial neural network with the physical features of the highest energy reconstructed shower as inputs. The $\nu_ e$ charged current identifier in this thesis utilizes a Long Short-Term Memory network with the physical features of every reconstructed shower in a particular interaction. In addition to the Long Short-Term Memory network, there are two Boosted Decision Trees to assist in event level selection. In the analysis of the data, 54 $\nu_e$ candidate events were detected with an expected background of 15 events. The results of this analysis prefer the normal mass hierarchy with maximal mixing and $\frac{\delta_{CP}}{\pi}=1.92_{-1.19}^{0.08}$. Results from this analysis differ from the published NOvA analysis, due to the differences of electron identification techniques.},
doi = {10.2172/1529330},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Thesis/Dissertation:
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