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Improvement of the nova near detector event reconstruction and primary vertexing through the application of machine learning methods
Elkarghli, Zakaria A.
Elkarghli, Zakaria A.
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2020-12
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The purpose of this work is to examine the application of a deep learning model in
event reconstruction of neutrino interactions. The challenges faced in event reconstruction
include the placement of an accurate primary neutrino interaction vertex which is used to
support the particle track and prong algorithms. The result of accurate primary vertex
ensures all particles involved in a neutrino interaction are included. We propose a
regression-based Convolutional Neural Network (CNN) method to predict the primary
vertex of a particle interaction. We show that with raw two-dimensional pixel map views as
input, the regression-based CNN can predict the primary vertex in all three coordinates.
This work is applied as part of the NOvA (NuMI Off-axis $\nu_e$ Appearance) near detector
reconstruction efforts. The primary vertex predicted by the regression-based CNN model
shows promising results for future applications. This deep learning method can be
extended to secondary vertexing through a Kernel Density Estimate algorithm discussed in
this work.
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Thesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and Physics
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Wichita State University
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© Copyright 2020 by Zakaria Elkarghli
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