Speaker
Description
Hyper-Kamiokande (HK) is the next generation neutrino observatory in Japan and the successor of Super-Kamiokande (SK) detector. It has been designed to extend the legacy of its predecessor into new realms of neutrino physics ranging from MeV (Solar or Supernovae neutrino) to several GeV energy scales, and in particular, discover CP violation for the very first time in the lepton sector. To reach these ambitious goals, HK will rely on a eight times larger target volume, enhanced photodetector capacity compared to SK, as well as precise calibration devices, setting the stage for breakthroughs in precision and computational performance.
In this context, the adoption of machine learning techniques for event reconstruction is tailored to exploit the full potential of HK. Specifically, graph neural networks (GNNs) allow for a more precise and faster event reconstruction with non-Euclidean geometry, ranging from low to high-energy events. In this talk, we will first present an overview of the different reconstruction algorithms used to reconstruct high-energy (GeV) events in HK. We will then show the very first and promising results of our GNN-based algorithm, and demonstrates it already boost the HK physics potential in the CP violation discovery range (GeV scale). We will conclude by presenting the prospects for the future HK reconstruction beyond the GeV scale.
| Type of contribution | Talk: 30 minutes. |
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