Abstract
KM3NeT is a distributed neutrino research infrastructure in the Mediterranean Sea. KM3NeT/ARCA is a high-energy neutrino telescope, dedicated to the search for extraterrestrial neutrino sources in the TeV-PeV range. One major goal is the identification of the sources of the neutrino flux recently discovered by IceCube. Furthermore, KM3NeT/ARCA is optimised to study Galactic neutrino point sources. The analysis of the flavour composition of astrophysical neutrinos arriving at Earth can shed light on the production mechanisms of these neutrinos inside their astrophysical sources. The distinction between different neutrino flavours is only possible on a statistical basis and a method called ``spectral fitting'' is employed to this end. In order to estimate the sensitivity of KM3NeT/ARCA to the flavour composition using this method, spectra obtained from Monte-Carlo-based pseudo-data samples are compared to expectations from neutrino flux models for signal and background. To increase the power of the spectral fitting procedure, the event sample is separated into multiple subsamples according to their event type. Therefore, an artificial neural network is used to discriminate between five target types: double bang events, cascades and three different track-like event types.
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