Speaker
Description
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the measured MiniBooNE low energy excess. The LArTPC technology pioneered by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision techniques can be used to process these images and aid in selecting $\nu_{e}$-CC and other rare signals from large cosmic and neutrino backgrounds. We present a new suite of deep learning tools to reconstruct neutrino interactions in MicroBooNE, with a focus on a convolutional neural network used to accurately assign labels to reconstructed particles. We will show that these techniques can be used to select $\nu_{e}$-CC events at purities and efficiencies that are competitive with the tools currently in use in MicroBooNE and that they have the potential to improve the sensitivity of future analyses.
