Speaker
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.
Several low-energy excess analyses are taking place in parallel, using independent reconstructions and selection schemes.
This talk 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.
I will present a novel 3D event reconstruction based on computer vision tools and a stochastic search algorithm that aims to reconstruct low energy events with high resolution.
I will then present validation studies verifying the good agreement of our simulation to our muon neutrino data.
