Conferences72nd Annual Meeting of the Physical Society of Japan, Toyonaka, 2017 (talk)
Big Data Science in Astroparticle Research Workshop, Aachen, 2018
DPG Frühjahrstagung der Sektion Materie und Kosmos, Würzburg, 2018 (talk)
Antares/KM3NeT collaboration meeting, Granada, 2018 (talk)
Neutrino 2018, Heidelberg, 2018 (poster)
FellowshipsDeutsche Physikalische Gesellschaft
CollaborationsKM3NeT Collaboration
Abstract of PhD project An important open question in neutrino physics is the unitarity of the PMNS matrix. Currently, the uncertainties on several matrix elements are too large to draw significant conclusions on the unitarity. This is mostly due to the low experimental statistics in the tau neutrino sector.

KM3NeT-ORCA is a water Cherenkov detector under construction with several megatons of instrumented volume. It will observe about 2400 tau neutrinos per year and it will significantly improve the available tau neutrino statistics. In ORCA, tau neutrinos will be identified by observing a statistical excess of cascade-like events with respect to the electron neutrino expectation from the atmosphere. Hence, the development of an algorithm for the separation of track-like charged-current muon neutrinos and cascade-like (other flavors) neutrino events is necessary.

Currently, event properties inspired by the different event types are used with shallow machine learning, in order to discriminate the two classes. Recent advances in computational performance have made it possible to employ deep artificial neural networks. In this approach, the experimental raw data is used for training a deep neural network. Here, the network builds a representation of the typical event properties that can be exploited to distinguish track-like from shower-like events.

The topic of my thesis is how we can employ Deep Learning within the Tau Appearance KM3NeT project.

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