Orateur
Description
KM3NeT consist of two water-Cherenkov neutrino detectors currently under construction in the Mediterranean Sea:
the low energy site ORCA in France, as well as the high energy site ARCA in Italy. ORCA's goal is the determination of the neutrino mass hierarchy by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. ARCA will use its wide coverage of the observable sky to look for high-energy astrophysical neutrino sources. Machine Learning algorithms play an important role in analysing the signatures induced by particles traversing the detectors.
This talk will give a detailed explanation of the types of data in KM3NeT, the challenges we faced when analysing it, and the various machine learning based solutions and workflows that were developed over the years. The topics range from maximum-likelihood-based reconstruction algorithms accompanied by shallow machine learning techniques like Random Forests, up to the use of deep artificial neural networks and graph-based networks.
