Date of Award
Doctor of Philosophy (PhD)
Charged Current Inclusive, Convolutional Neural Networks, Cross Section, MicroBooNE, Muon Neutrino
Physical Sciences and Mathematics
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.
Esquivel, Jessica Nicole, "Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE
Charged Current Inclusive Cross Section Measurement." (2018). Dissertations - ALL. 845.