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Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector
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Atmospheric neutrino oscillations are important to the study of neutrino properties, including the neutrino mass ordering problem. A good capability to identify neutrinos' flavor and neutrinos against antineutrinos is crucial in such measurements. In this paper, we present a machine-learning-based approach for identifying atmospheric neutrino events in a large homogeneous liquid scintillator detector. This method identifies features of PMT waveforms that reflect event topologies and uses them as input to machine learning models. In addition, neutron-capture information is utilized to achieve neutrino versus antineutrino discrimination. Preliminary performances based on Monte Carlo simulations are presented, which demonstrate such a detector's potential in future measurements of atmospheric neutrinos such as the one planned for the JUNO experiment.
Forward citations
Cited by 2 Pith papers
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Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
Neural density estimators paired with nested sampling recover JUNO's energy response parameters with bias below 0.55% and uncertainties consistent with statistics, in simulation-based closure tests.
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Ultralight dark matter search in a large liquid scintillator detector
A JUNO-like detector could constrain the neutrino–ultralight-dark-matter oscillation-modulation parameters to ηΔ21 < 2.5×10^-2 and ηΔ31 < 5×10^-3 at 90% CL.
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