Reconstruction of muon bundles in KM3NeT detectors using machine learning methods
This work addresses the challenge of analyzing multi-muon events for neutrino astronomy and oscillation studies, representing an incremental application of existing methods to new detector data.
The study tackled the reconstruction of muon bundles in KM3NeT neutrino detectors using machine learning, achieving predictions for the total number of muons, their total energy, and the energy of the primary cosmic ray.
The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.