Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) will measure all long-baseline neutrino oscillation parameters, including the CP-violating phase and the neutrino mass ordering, in one single experiment. DUNE will also study astrophysical neutrinos and perform a broad range of new physics searches. This ambitious programme is enabled by the very high-resolution imaging capabilities of Liquid-Argon Time-Projection Chambers (LArTPCs). In order to fully exploit the LArTPC capabilities, sophisticated reconstruction techniques are required to tackle a wide range of challenging event topologies that have a critical impact on the flagship goals at DUNE, such as overlapping photon showers from neutral pions. This talk will describe expanding the multi-algorithm Pandora reconstruction with a novel reclustering approach that capitalises on deep learning techniques such as Graph Neural Networks to tackle these topologies and boost the pattern recognition performance.
| Type of contribution | Talk: 15 minutes. |
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