First Deep Learning based Event Reconstruction for Low-Energy Excess Searches with MicroBooNE | Zendy

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

Publication year - 2018

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1573220

Subject(s) - fermilab , event reconstruction , detector , event (particle physics) , physics , neutrino , nuclear physics , track (disk drive) , particle physics , energy (signal processing) , beam (structure) , neutrino detector , computer science , neutrino oscillation , optics , astrophysics , operating system , quantum mechanics

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 chargedcurrent 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.

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