Speaker
Description
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 hierarchy by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. Additionally, the detector will observe a large amount of atmospheric muons, which can be used to study the properties of extensive air showers and cosmic ray particles.
Deep Learning techniques provide promising methods to analyse the signatures induced by the particles traversing the detector. Despite being in an early stage of construction, the data taken so far provide large statistics to investigate the signatures from atmospheric muons. This contribution will cover a deep-learning based approach using graph convolutional networks. Reconstructions of the properties of atmospheric muons like the bundle multiplicity that can aid in studying the primary cosmic ray interactions are presented. Furthermore, the performances are compared to the ones of classical approaches.
Keywords
Atmospheric muon, deep learning, graph, KM3NeT, Orca
| Subcategory | Experimental Methods & Instrumentation |
|---|---|
| Collaboration | KM3NeT |
