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

Thesis/Dissertation · Mon Jan 01 04:00:00 EST 2018

DOI:https://doi.org/10.2172/1437290· OSTI ID:1437290

Esquivel, Jessica Nicole ^[1]

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1. Syracuse U.

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.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1437290
• Report Number(s): FERMILAB-THESIS-2018-06; 1672891
• Country of Publication: United States
• Language: English