Description
The Jiangmen Underground Neutrino Observatory (JUNO), is a 20 kton multi-purpose liquid-scintillator detector to be completed in 2023. Its main goal is the determination of the neutrino mass ordering using the measurement of the vacuum-dominated oscillation pattern of reactor anti-neutrinos from two nearby nuclear power plants. The sensitivity of JUNO to the neutrino mass ordering can be enhanced via a combined analysis of reactor anti-neutrinos with atmospheric neutrinos, in which the matter-dominated oscillation depends on the mass ordering. Such an analysis requires a precise reconstruction of the energy and the direction of atmospheric neutrinos. As the largest liquid-scintillator detector to be built, JUNO will also be able to measure the atmospheric neutrino flux down to lower energies than the current large water/ice Cherenkov detectors. This poster presents the reconstruction of the energy of atmospheric neutrinos with a machine learning approach and the direction reconstruction with a traditional approach. Both approaches are a Monte Carlo based study, the latter focuses on the reconstruction of the photon emission topology in the JUNO detector, while the machine learning approach relies on the geometrical representation of the detector with a Graph Convolutional Neural Network.
