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T: Fachverband Teilchenphysik

T 7: Data, AI, Computing, Electronics I

T 7.2: Vortrag

Montag, 16. März 2026, 16:30–16:45, KH 00.024

Reconstruction and Identification of Atmospheric Neutrino Events at JUNO Using Machine-Learning Methods — •Milo Charavet, Caren Hagner, Daniel Bick, and Mikhail Smirnov — University of Hamburg, Hamburg, Germany

The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation multipurpose liquid scintillator detector with a 20-kiloton target mass, located in southern China. One of its primary goals is to determine the neutrino mass ordering (NMO) with a significance of at least 3σ by precisely measuring the oscillation pattern of reactor antineutrinos over a 53 km baseline. While JUNO's NMO sensitivity primarily comes from reactor neutrinos, atmospheric neutrino oscillations provide complementary sensitivity via matter effects and can enhance the overall performance in a joint analysis. The construction of the detector has been completed, and JUNO has already started physics data taking since August 2025. Detecting atmospheric neutrinos in a liquid scintillator detector poses several challenges, such as the reconstruction of complex event topologies and the separation of interaction channels. Advanced machine learning methods, in particular deep-learning based reconstruction techniques, offer promising solutions to address these difficulties. This talk will present recent progress in using such methods to reconstruct the energy, direction, and vertex of atmospheric neutrino events, as well as their performance in particle identification from Monte Carlo studies, highlighting both the challenges and the advantages of these innovative approaches.

Keywords: JUNO; atmospheric neutrinos; simulation; reconstruction