Séminaires

# Adrien Hourlier (MIT) : 3D track finding for MicroBooNE’s deep learning based event reconstruction chain

Europe/Paris
Description
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. Our
low-energy excess analysis makes use of deep learning algorithms
applied to the high-resolution images provided by the MicroBooNE
LArTPC. I will present a novel 3D event reconstruction based on
computer vision tools and a stochastic search algorithm, yielding
an excellent energy resolution for 1mu1p muon neutrino
interactions in the [200-1500] MeV range. I will then present
validation studies verifying the good agreement of our simulation
to our muon neutrino data.
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