3D track finding for MicroBooNE’s deep learning based event reconstruction chain
Adrien Hourlier - MIT
Mon, Nov. 12th 2018, 11:00-12:00
Bat 141, salle André Berthelot, CEA Paris-Saclay

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 a 2.5% 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.

Contact : Fabrice BALLI


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