Speaker
Description
KM3NeT/ARCA and KM3NeT/ORCA are the new generation of neutrino telescopes located in the depths of the Mediterranean Sea. Each comprises a grid of optical sensors that capture the Cherenkov light emitted by charged particles produced in neutrino interactions. KM3NeT/ARCA, sensitive to interactions with energies ranging from TeV to PeV, focuses on cosmic neutrinos, while KM3NeT/ORCA investigates atmospheric neutrino oscillations in the
GeV energy range.
These detectors analyse light patterns to infer the direction, energy, and position of neutrino interactions. While likelihood-based methods have traditionally been used for reconstruction, recent advancements in machine learning offer compelling alternatives. Furthermore, detecting neutrinos becomes particularly challenging when dealing with high background rates in the detector. Machine learning techniques have been developed not only to categorise events based on the nature of the interaction, but also to classify events as background or signal.
These machine learning techniques encompass a variety of approaches, ranging from Boosted Decision Trees to deep learning techniques. This contribution aims to provide an overview of the primary machine learning algorithms currently employed in KM3NeT. In addition, it will explore future developments, such as the introduction of transformers for
event reconstruction and the implementation of GraphNeT for event selection and reconstruction.
| Type of contribution | Talk: 15 minutes. |
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