Graph neural networks for reconstruction and classification in KM3NeT

Journal of Instrumentation, vol. 16, pp. C10011

Abstract

Abstract KM3NeT, a neutrino telescope currently under construction in the Mediterranean Sea, consists of a network of large-volume Cherenkov detectors. Its two different sites, ORCA and ARCA, are optimised for few GeV and TeV-PeV neutrino energies, respectively. This allows for studying a wide range of physics topics spanning from the determination of the neutrino mass hierarchy to the detection of neutrinos from astrophysical sources. Deep learning techniques provide promising methods to analyse the signatures induced by charged particles traversing the detector. This document will cover a deep learning based approach using graph convolutional networks to classify and reconstruct events in both the ORCA and ARCA detector. Performance studies on simulations as well as applications to real data will be presented, together with comparisons to classical approaches.

Authors 4

1. S. Reck corresponding

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Erlangen Centre for Astroparticle Physics (ECAP), Universität Erlangen-Nürnberg, Erwin-Rommel-Str. 1, 91058 Erlangen, Germany

2. Daniel Guderian

   University of Münster

   Affiliation as printed

   WWU Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, Germany

3. Gijs Vermariën Aachen Leiden Observatory

   Leiden University · National Institute for Subatomic Physics · Leiden Observatory

   Affiliation as printed

   Dutch National Institute for Subatomic Physics (Nikhef), Science Park 105, Amsterdam, The Netherlands

   Leiden Observatory, Leiden University, Niels Bohrweg 2, Leiden, The Netherlands

4. Alba Domi

   National Institute for Subatomic Physics · Institute of Physics · University of Amsterdam

   Affiliation as printed

   Dutch National Institute for Subatomic Physics (Nikhef), Science Park 105, Amsterdam, The Netherlands

   University of Amsterdam, Institute of Physics, Science Park 904, Amsterdam, The Netherlands