Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Abi, B.Alonso Monsalve, SaúlGarcía Carballeira, FélixDUNE2023-07-142023-07-142020-11-01Abi, B., et al. (2020). Neutrino interaction classification with a convolutional neural network in the dune far detector. Physical review D, 102(9), 092003.2470-0010https://hdl.handle.net/10016/37844Documento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.20eng© The Authors, 2020.Atribución 3.0 EspañaNeutrino interactionsNeutrino oscillationsNeutrinosNeutrino detectorsNeutrino interaction classification with a convolutional neural network in the DUNE far detectorresearch articleInformáticaTelecomunicacioneshttps://doi.org/10.1103/PhysRevD.102.092003open access092003-19092003-20Physical review D102AR/0000027714