Speaker
Description
Identification of electron neutrino interactions in liquid argon time projection chambers is essential to seeking answers to questions of the fundamental nature of neutrinos. These analyses include determining the ordering of the mass states and the value of the CP-violating phase in the neutrino sector in the Deep Underground Neutrino Experiment (DUNE), and performing neutrino oscillation measurements and beyond the Standard Model searches in the Fermilab Short-Baseline Neutrino Program. The deep learning approach based on a convolutional neural network for highly efficient and pure selections of charged-current neutrino interactions forms a key part of neutrino oscillation analysis sensitivities in DUNE. It is important to test the network performance on real neutrino data. The ArgoNeuT experiment has collected GeV-scale neutrino/antineutrino data, which has been used to investigate the deep-learning based identification of charged-current electron neutrino interactions. We will show the reconstruction performance using different readout planes and compare that with the traditional electron neutrino classification method developed in the ArgoNeuT experiment.
| Collaboration | ArgoNeuT Collaboration |
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