Event identification for KM3NeT/ARCA

Abstract

KM3NeT is a large research infrastructure consisting of a network of deep-sea neutrino telescopes. KM3NeT/ARCA will be the instrument detecting high-energy neutrinos with energies above 100 TeV. This instrument gives a new opportunity to observe the neutrino sky with very high angular resolution to be able to detect neutrino point sources. Furthermore it will be possible to probe the flavour composition of neutrino fluxes, and hence production mechanisms, with so-far unreached precision. Neutrinos produce different event topologies in the detector according to their flavour, interaction channel and deposited energy. Machine-learning algorithms are able to learn features of topologies to discriminate them. In previous analyses only two event types were regarded, namely the shower and track topology. With good timing resolution and precise reconstruction algorithms it is possible to separate into more event types, for example the double bang topology produced by tau neutrinos. The final goal is to distinguish all three neutrino flavors as much as possible. To resolve this issue the KM3NeT collaboration uses deep neural networks trained with Monte Carlo events of all neutrino types. This contribution shows the ability of KM3NeT/ARCA to classify events in more than two neutrino event topologies. Furthermore, the borders between detectable classes are shown, such as the minimum distance the tau has to travel before decaying into a tau neutrino to be detected as double bang event.