Date of Award
May 2018
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Physics
Advisor(s)
Mitchell Soderberg
Keywords
Charged Current Inclusive, Convolutional Neural Networks, Cross Section, MicroBooNE, Muon Neutrino
Subject Categories
Physical Sciences and Mathematics
Abstract
The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.
Access
Open Access
Recommended Citation
Esquivel, Jessica Nicole, "Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE
Charged Current Inclusive Cross Section Measurement." (2018). Dissertations - ALL. 845.
https://surface.syr.edu/etd/845
