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dc.contributor.advisorMuether, Mathew
dc.contributor.authorElkarghli, Zakaria A.
dc.date.accessioned2021-02-02T15:56:14Z
dc.date.available2021-02-02T15:56:14Z
dc.date.issued2020-12
dc.identifier.othert20048
dc.identifier.urihttps://soar.wichita.edu/handle/10057/19755
dc.descriptionThesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and Physics
dc.description.abstractThe 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.
dc.format.extentxi, 58 pages
dc.language.isoen_US
dc.publisherWichita State University
dc.rights© Copyright 2020 by Zakaria Elkarghli All Rights Reserved
dc.subject.lcshElectronic dissertations
dc.titleImprovement of the nova near detector event reconstruction and primary vertexing through the application of machine learning methods
dc.typeThesis


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