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Machine learning in NO$\nu$A near detector vertexing
Khan, Momin
Khan, Momin
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2022-05
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Abstract
This 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.
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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 2022 by Momin Khan
All Rights Reserved