Heidelberg 2022 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 107: Data Analysis, Information Technology and Artificial Intelligence 5

T 107.3: Vortrag

Donnerstag, 24. März 2022, 16:45–17:00, T-H39

Tau neutrino selection with Graph Neural Networks for KM3NeT/ORCA — •Lukas Hennig for the ANTARES-KM3NET-ERLANGEN collaboration — Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Centre for Astroparticle Physics, Erwin-Rommel-Straße 1, 91058 Erlangen, Germany

One of the goals of the KM3NeT collaboration is to constrain the PMNS matrix elements associated with the tau neutrino flavor. The data needed to perform this task is taken by KM3NeT’s ORCA detector, a water Cherenkov neutrino detector currently under construction in the deep Mediterranean Sea. To constrain the matrix elements, one needs to measure the tau neutrino flux produced by atmospheric muon and electron neutrinos oscillating into tau neutrinos. Selecting the tau neutrino events from the full neutrino event dataset is a notoriously difficult task because the final states of a tau neutrino interaction on a nucleon or nucleus look very similar to those produced in other CC and NC neutrino interactions. This classification problem is addressed by using Graph Neural Networks, a type of neural network architecture that has shown promising results, e.g., in the related task of jet tagging. This presentation will explain how GNNs are applied to neutrino telescope data and report the first results concerning the classification performance.