Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks

Discussion timeslot (ZOOM-Meeting): 15. July 2021 - 18:00
ZOOM-Meeting URL: https://desy.zoom.us/j/91999581729
ZOOM-Meeting ID: 91999581729
ZOOM-Meeting Passcode: ICRC2021
Corresponding Session: https://icrc2021-venue.desy.de/channel/37-Reconstruction-amp-Analysis-Techniques-NU/126
Live-Stream URL: https://icrc2021-venue.desy.de/livestream/Discussion-05/6

Abstract:
"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.rnrnDeep 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."

Authors: Stefan Reck
Collaboration: KM3NeT

Indico-ID: 566
Proceeding URL: https://pos.sissa.it/395/1048