Heidelberg 2022 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 20: Neutrino Physics without Accelerators 1

T 20.6: Vortrag

Montag, 21. März 2022, 17:40–17:55, T-H33

Machine learning based reconstruction of atmospheric neutrino events in JUNO — •Rosmarie Wirth — Hamburg University, Hamburg, Germany

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kt liquid scintillation detector. By observing reactor anti-neutrinos with a 53 km baseline, JUNO aims to determine the mass hierarchy with 3 σ significance.
Due to JUNOs large volume, it could be suitable to measure atmospheric neutrino events with high precision. In that case, this channel could deliver further measurements on the mass ordering and the atmospheric oscillation parameters. To obtain this goal sufficient reconstruction methods are needed. This talk presents machine learning based reconstruction methods to analyze these atmospheric neutrino events at JUNO.