Open AccessLow energy muon neutrino reconstruction in MicroBooNE
Author(s) -
A. Hourlier
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1312/1/012007
Subject(s) - neutrino , physics , miniboone , time projection chamber , muon , particle physics , nuclear physics , beamline , neutrino detector , fermilab , muon neutrino , electron neutrino , neutrino oscillation , measurements of neutrino speed , range (aeronautics) , event (particle physics) , sterile neutrino , electron , solar neutrino , beam (structure) , optics , astrophysics , aerospace engineering , engineering
MicroBooNE is a Liquid Argon Time Projection Chamber (LArTPC) neutrino experiment on the Booster Neutrino Beamline at the Fermi National Accelerator Laboratory, with an 85-tonne active mass. One of MicroBooNE’s primary physics goals is to investigate the excess of electron neutrino events seen by MiniBooNE in the [200-600] MeV range. MicroBooNE will constrain the intrinsic electron neutrino component of the beam by measuring the muon neutrino spectrum. Several low-energy excess analyses are taking place in parallel, using independent reconstructions and selection schemes. This paper will focus on a low-energy excess analysis that makes use of deep learning algorithms applied to the high-resolution images provided by the MicroBooNE LArTPC. We present a novel 3D event reconstruction based on computer vision tools and a stochastic search algorithm, yielding a 2.2% energy resolution for 1 μ 1p muon neutrino interactions in the [200-1500] MeV range.