Graph neural networks for reconstruction and classification in KM3NeT

Reck S, Guderian D, Vermarien G, Domi A (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Journal of Instrumentation Institute of Physics: Hybrid Open Access

Book Volume: 16

Article Number: A3

Journal Issue: 10

DOI: 10.1088/1748-0221/16/10/C10011

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 with CRIS profile

Stefan Reck Lehrstuhl für Experimentalphysik (Astroteilchenphysik) Alba Domi Lehrstuhl für Experimentalphysik (Astroteilchenphysik)

Involved external institutions

University of Amsterdam NL Netherlands (NL) Westfälische Wilhelms-Universität (WWU) Münster DE Germany (DE) Leiden University NL Netherlands (NL)

How to cite

APA:

Reck, S., Guderian, D., Vermarien, G., & Domi, A. (2021). Graph neural networks for reconstruction and classification in KM3NeT. Journal of Instrumentation, 16(10). https://doi.org/10.1088/1748-0221/16/10/C10011

MLA:

Reck, Stefan, et al. "Graph neural networks for reconstruction and classification in KM3NeT." Journal of Instrumentation 16.10 (2021).

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