Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation large liquid-scintillator neutrino detector. Its primary goal is the determination of neutrino mass ordering (NMO). JUNO's main sensitivity to NMO comes from reactor neutrino measurements. However, high-energy (GeV level) atmospheric neutrino measurements can also independently probe NMO, potentially enhancing JUNO's overall sensitivity through a comprehensive analysis.
Recently, a multi-purpose machine learning method for GeV events has been developed, yielding promising results for reconstructing the neutrino directionality, energy and vertex, as well as identifying neutrino flavors.
The matter on neutrino oscillations depends on the direction reconstruction and flavor separation performance. Therefore, the implementation of these machine learning algorithms will lead to an enhancement of the NMO sensitivity.
This poster presents a multi-purpose machine learning method for atmospheric neutrinos and the prospects of JUNO's NMO sensitivity from atmospheric neutrino measurements.
| Collaboration / Activity | JUNO Collaboration |
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