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dc.contributor.advisorMuether, Mathew
dc.contributor.authorKhan, Momin
dc.date.accessioned2022-06-20T16:31:27Z
dc.date.available2022-06-20T16:31:27Z
dc.date.issued2022-05
dc.identifier.othert22013
dc.identifier.urihttps://soar.wichita.edu/handle/10057/23454
dc.descriptionThesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and Physics
dc.description.abstractThis work presents an alternative method for a reconstruction machine learning algorithm in place of the one currently used by the NO$\nu$A (NuMI Off-axis $\nu_e$ Appearance) group for the analysis of neutrino events at the Fermilab near and far detectors. Current reconstruction methods are flawed because there are many challenges associated with finding the proper vertex of a neutrino event including background noise, incorrect prong creation, and secondary vertecies. This work presents a regression-based convolutional neural network (CNN) that analyzes 2-dimensional pixel maps from NO$\nu$A ’s catelog of forward horn current (FHC) and reverse horn current (RHC) h5 files and more accurately predicts the location of the vertex for each coordinate direction. Additionally, this model can be implemented into NO$\nu$A using their primary framework, NO$\nu$ASOFT and applied to larger data sets to get a better comparison, or even to expand the model to take on secondary vertexing.
dc.format.extentxiv, 73 pages
dc.language.isoen_US
dc.publisherWichita State University
dc.rights© Copyright 2022 by Momin Khan All Rights Reserved
dc.subject.lcshElectronic dissertations
dc.titleMachine learning in NO$\nu$A near detector vertexing
dc.typeThesis


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