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30 May 2022 to 4 June 2022
Virtual Seoul
Asia/Seoul timezone

A deep-learning based charged-current electron neutrino interaction identification in the ArgoNeuT experiment

Not scheduled
Virtual Seoul

Virtual Seoul

Poster Neutrino interactions Poster


Wanwei Wu (Fermi National Accelerator Laboratory)


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

Primary authors

Wanwei Wu (Fermi National Accelerator Laboratory) Dr Saul Alonso Monsalve (ETH Zurich) Dr Leigh Whitehead (University of Cambridge) Dr Tingjun Yang (Fermi National Accelerator Laboratory)

Presentation Materials