Graph Neural Networks for reconstruction and classification in KM3NeT

TL;DR

This paper explores the use of Graph Convolutional Networks to improve event classification and reconstruction in the KM3NeT neutrino telescope, demonstrating promising results on simulated and real data.

Contribution

It introduces a novel Deep Learning approach using Graph Convolutional Networks for event analysis in KM3NeT, outperforming classical methods.

Findings

Enhanced event classification accuracy

Effective reconstruction of particle trajectories

Successful application to real detector data

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.