Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks

• Main Authors: Ageron, M., Aiello, S, Albert, Arthur, Alshamsi, Mohammed, Ambrosone, Antonio, André, Michel, Androulakis, Giorgos, Anghinolfi, Marco, Anton, Gisela, Ardid Ramírez, Miguel, Aublin, J., Bagatelas, Christos
• Other Authors: Centre Tecnològic de Vilanova i la Geltrú, Universitat Politècnica de Catalunya. LAB - Laboratori d'Aplicacions Bioacústiques
• Format: Conference Object
• Language: English
• Published: arXiv 2021
• Subjects: Àrees temàtiques de la UPC::Física::Astronomia i astrofísica; Neutrinos; Cherenkov radiation; Astrophysics; Telescopes; Charged particles; Convolution; Cosmic rays; Deep learning; Neutrons; Particle detectors; Atmospheric muons; Cherenkov; Convolutional networks; Cosmic-ray particles; Energy; Mass hierarchy; Mediterranean sea; Neutrino detectors; Neutrino mass; Zenith angle; Cosmology; Neutrins; Astrofísica; Telescopis; Radiació sincrotrònica; Orca
• Online Access: http://hdl.handle.net/2117/385730

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 observes a large amount of atmospheric muons, which can be used to study extensive air showers generated by cosmic ray particles. This work describes a deep-learning based reconstruction of atmospheric muons using graph convolutional networks. They are used to reconstruct the zenith angle, the muon multiplicity and the diameter of atmospheric muon bundles. Simulations and measured data from an early four line stage of the detector are used to evaluate the performance. Furthermore, the reconstructions are compared to the ones of classical approaches, and use cases for the indirect study of cosmic ray particles are shown. Article signat per 297 autors/es: M.Ageron, S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre, Z. Aly, A. Ambrosone, F. Ameli, M. Andre, G. Androulakis, M. Anghinolfi, M. Anguita, G. Anton, M. Ardid, S. Ardid, W. Assal, J. Aublin, C. Bagatelas, B. Baret, S. Basegmez du Pree, M. Bendahman, F. Benfenati, E. Berbee, A. M. van den Berg, V. Bertin, S. Beurthey, V. van Beveren, S. Biagi, M. Billault, M. Bissinger, M. Boettcher, M. Bou Cabo, J. Boumaaza, M. Bouta, C. Boutonnet, G. Bouvet, M. Bouwhuis, C. Bozza, H.Brânzas, R. Bruijn, J. Brunner, R. Bruno, E. Buis, R. Buompane, J. Busto, B. Caiffi, L. Caillat, D. Calvo, S. Campion, A. Capone, H. Carduner, V. Carretero, P. Castaldi, S. Celli;, R. Cereseto, M. Chabab, C. Champion, N. Chau, A. Chen, S. Cherubini, V. Chiarella, T. Chiarusi, M. Circella, R. Cocimano, J. A. B. Coelho, A. Coleiro, M. Colomer Molla, S. Colonges, R. Coniglione, A. Cosquer, P. Coyle, M. Cresta, A. Creuso, A. Cruz, G. Cuttone, A. D’Amico, R. Dallier, B. De Martino, M. De Palma, I. Di Palma, A. F. Díaz, D. Diego- Tortosa, C. ...