3D-Reconstruction of Tau Neutrinos in LArTPC Detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment currently under construction. DUNE will consist of two high-resolution neutrino interaction imaging detectors exposed to the world’s most intense neutrino beam, with the Near Detector at Fermilab and the Far Detector 1,300 km away at the Sanford Underground Research Facility in South Dakota.

DUNE's liquid argon ⁴⁰Ar far detector offers high statistics and exceptional resolution, enabling precision studies of oscillation parameters, searching for CP violation in the lepton sector, testing interaction models, and investigating complex phenomena like tau neutrino detection. This capability is crucial for completing the three-flavor neutrino paradigm and addressing various open questions, such as non-standard neutrino interactions, the unitarity of the PMNS matrix, sterile neutrinos, and neutrino interactions.

In analyzing LArTPC data, detector hits can be viewed as nodes in a graph, with edges representing their spatial and temporal relationships. We can enhance particle identification and reconstruction accuracy by employing graph neural networks (GNNs). In my presentation, I will focus on tau neutrino reconstruction and demonstrate the effectiveness of our in-house developed GNN, NuGraph. This system classifies detector hits based on the type of particle that generated them, ensuring accurate identification and categorization of data according to unique characteristics.