MicroBooNE is a short baseline neutrino experiment situated at
Fermilab. It measures neutrinos from the Booster Neutrino Beam (BNB)
with a 85t Liquid Argon Time Projection Chamber (LArTPC) detector. The
main scientific goals of MicroBooNE are to measure neutrino-LAr
interaction cross-sections and to explore the low energy electron
neutrino spectrum. Neutrino cross section measurements on LAr are an
important input for the DUNE/LBNF program. Measuring low energy
electron neutrinos is aimed at probing the excess of neutrino-like
events observed by the MiniBooNE experiment in the [200,600] MeV
region, which may hint at BSM physics.
Several low-energy excess analyses are taking place in parallel in
MicroBooNE, using independent event reconstruction techniques and
different signal topologies. The Deep Learning (DL) group targets 1e1p
events and employs machine learning techniques to extract information
used in event reconstruction.
In this talk I will present the current status of the DL low-energy
excess analysis and talk about our application of machine learning to
LArTPC images.