Muon/Pion separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement.

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Year

2018

Author dc:creator

• Esquivel, Jessica Nicole

Contributors dc:contributor

• Mitchell Soderberg

• Charged Current Inclusive
• Convolutional Neural Networks
• Cross Section, MicroBooNE
• Muon Neutrino
• Physical Sciences and Mathematics

Repository record dc:identifier

https://surface.syr.edu/etd/845

OAI identifier oai:identifier

oai:surface.syr.edu:etd-1846