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Title: $$\mu / \pi$$ Separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement

Abstract

The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate $$\mu^{\prime}$$s and $$\pi^{\prime}$$s for use in increasing the acceptance rate of $$\mu^{\prime}$$s 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.

Authors:
 [1]
  1. Syracuse U.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1437290
Report Number(s):
FERMILAB-THESIS-2018-06
1672891
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English

Citation Formats

Esquivel, Jessica Nicole. $\mu / \pi$ Separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement. United States: N. p., 2018. Web. doi:10.2172/1437290.
Esquivel, Jessica Nicole. $\mu / \pi$ Separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement. United States. doi:10.2172/1437290.
Esquivel, Jessica Nicole. Mon . "$\mu / \pi$ Separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement". United States. doi:10.2172/1437290. https://www.osti.gov/servlets/purl/1437290.
@article{osti_1437290,
title = {$\mu / \pi$ Separation using Convolutional Neural Networks for the MicroBooNE Charged Current Inclusive Cross Section Measurement},
author = {Esquivel, Jessica Nicole},
abstractNote = {The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate $\mu^{\prime}$s and $\pi^{\prime}$s for use in increasing the acceptance rate of $\mu^{\prime}$s 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.},
doi = {10.2172/1437290},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}

Thesis/Dissertation:
Other availability
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