Promotionsvortrag Physik: „Cosmic ray composition measurement using Graph Neural Networks for KM3NeT/ORCA“

Ankündigung des Promotionsvortrags von: Herrn Stefan Reck

The chemical composition of cosmic rays arriving at the Earth's atmosphere at high energies in the PeV region and beyond has been the research target of various experiments, but is still subject to large uncertainties.

A precise understanding of the high energy composition allows to constrain the origin of cosmic rays and the processes by which such high energies can be reached in astronomical accelerators.

KM3NeT/ORCA is a water-Cherenkov neutrino detector, currently under construction in the Mediterranean Sea at a depth of 2450 meters.

The project's main goal is the determination of the neutrino mass ordering by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. However, the majority of the particles observed by the detector are atmospheric muons.

While they are a source of background for neutrino measurements, they can also be used to indirectly study the properties of extensive air showers and cosmic ray particles.

In this talk, a set of novel reconstructions for muons is presented, which makes use of deep artificial neural networks to analyze low-level experimental data and directly reconstruct the desired properties, independent of existing approaches.

Detailed comparisons between the performance on simulations and on measured data for a small version of the detector show decent agreement.

Using these new reconstructions, for the first time in KM3NeT an indirect measurement of the mass composition of cosmic rays is successfully performed on six months of measured data.

(Vortrag auf Englisch)

Dem Vortrag schließt sich eine Diskussion von 15 Minuten an. Vortrag und Diskussion sind öffentlich. Diesen Verfahrensteilen folgt ein nicht öffentliches Rigorosum von 45 Minuten.