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Title: First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE

Abstract

This paper describes algorithms developed to isolate and accurately reconstruct two-track νµ-like events that are contained within the MicroBooNE detector. This reconstruction has applications to searches for neutrino oscillations and measurements of cross sections using events that are charged-current quasi-elastic-like, among other applications. The algorithms we discuss will be applicable to all detectors running in Fermilab’s SBN program, and any future LArTPC experiment with beam energies ~ 1 GeV.

Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Org.:
MicroBooNE Collaboration
OSTI Identifier:
1573220
Report Number(s):
FERMILAB-MICROBOONE-NOTE-1042-PUB; MICROBOONE-NOTE-1042-PUB
oai:inspirehep.net:1763005; TRN: US2000099
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

None, None. First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. United States: N. p., 2018. Web. doi:10.2172/1573220.
None, None. First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE. United States. https://doi.org/10.2172/1573220
None, None. 2018. "First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE". United States. https://doi.org/10.2172/1573220. https://www.osti.gov/servlets/purl/1573220.
@article{osti_1573220,
title = {First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE},
author = {None, None},
abstractNote = {This paper describes algorithms developed to isolate and accurately reconstruct two-track νµ-like events that are contained within the MicroBooNE detector. This reconstruction has applications to searches for neutrino oscillations and measurements of cross sections using events that are charged-current quasi-elastic-like, among other applications. The algorithms we discuss will be applicable to all detectors running in Fermilab’s SBN program, and any future LArTPC experiment with beam energies ~ 1 GeV.},
doi = {10.2172/1573220},
url = {https://www.osti.gov/biblio/1573220}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jul 09 00:00:00 EDT 2018},
month = {Mon Jul 09 00:00:00 EDT 2018}
}