Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

KM3NeT Collaboration

Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

KM3NeT Collaboration

Physics

University of Bologna
National Institute for Nuclear Physics
Demokritos National Centre for Scientific Research
Friedrich-Alexander University Erlangen-Nürnberg
Dipartimento di Fisica E.R. Caianiello
Université de Strasbourg
Université de Haute-Alsace
University of Valencia
Aix-Marseille Université
University of Naples Federico II
Polytechnic University of Catalonia
University of Granada
Nantes Université
Polytechnic University of Valencia
Mohammed V University in Rabat
Université de Paris
Université de Caen
Czech Technical University in Prague
Comenius University
National Institute for Subatomic Physics
University of Campania Luigi Vanvitelli
University of Hull
North West University
Mohamed I University
University of Salerno
Institute for Space Sciences
University of Amsterdam
Netherlands Organisation for Applied Scientific Research
University of Rome La Sapienza
Cadi Ayyad University
University of the Witwatersrand
University of Catania
International Centre for Radio Astronomy Research
University of Würzburg
Western Sydney University
LPC
University of Genoa
Royal Netherlands Institute for Sea Research - NIOZ
National Centre for Nuclear Research
Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences
Ivane Javakhishvili Tbilisi State University
The University of Georgia, Tbilisi
Institut universitaire de France
IN2P3 - Institut National de Physique Nucléaire et de Physique Des Particules
Leiden University
Université Montpellier 2

Research output: Contribution to journal › Conference article › peer-review

Abstract

KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.

Original language	English
Article number	1194
Journal	Proceedings of Science
Volume	444
Publication status	Published - 27 Sept 2024
Event	38th International Cosmic Ray Conference, ICRC 2023 - Nagoya, Japan Duration: 26 Jul 2023 → 3 Aug 2023

ASJC Scopus subject areas

Multidisciplinary

Cite this

@article{52cff9b1111e4f0abea1de2500b70c83,

title = "Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8",

abstract = "KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.",

author = "\{KM3NeT Collaboration\} and Francesco Filippini and Eleni Androutsou and Alba Domi and Bernardino Spisso and Evangelia Drakopoulou and S. Aiello and A. Albert and \{Alves Garre\}, S. and Z. Aly and A. Ambrosone and F. Ameli and M. Andre and E. Androutsou and M. Anguita and L. Aphecetche and M. Ardid and S. Ardid and H. Atmani and J. Aublin and L. Bailly-Salins and Z. Barda{\v c}ov{\'a} and B. Baret and A. Bariego-Quintana and \{Basegmez du Pree\}, S. and Y. Becherini and M. Bendahman and F. Benfenati and M. Benhassi and Benoit, \{D. M.\} and E. Berbee and V. Bertin and S. Biagi and M. Boettcher and D. Bonanno and J. Boumaaza and M. Bouta and M. Bouwhuis and C. Bozza and Bozza, \{R. M.\} and H. Br{\^a}nza{\c s} and F. Bretaudeau and R. Bruijn and J. Brunner and R. Bruno and E. Buis and R. Buompane and J. Busto and B. Caiffi and D. Calvo and S. Razzaque",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons.; 38th International Cosmic Ray Conference, ICRC 2023 ; Conference date: 26-07-2023 Through 03-08-2023",

year = "2024",

month = sep,

day = "27",

language = "English",

volume = "444",

journal = "Proceedings of Science",

issn = "1824-8039",

publisher = "Sissa Medialab Srl",

}

TY - JOUR

T1 - Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

AU - KM3NeT Collaboration

AU - Filippini, Francesco

AU - Androutsou, Eleni

AU - Domi, Alba

AU - Spisso, Bernardino

AU - Drakopoulou, Evangelia

AU - Aiello, S.

AU - Albert, A.

AU - Alves Garre, S.

AU - Aly, Z.

AU - Ambrosone, A.

AU - Ameli, F.

AU - Andre, M.

AU - Androutsou, E.

AU - Anguita, M.

AU - Aphecetche, L.

AU - Ardid, M.

AU - Ardid, S.

AU - Atmani, H.

AU - Aublin, J.

AU - Bailly-Salins, L.

AU - Bardačová, Z.

AU - Baret, B.

AU - Bariego-Quintana, A.

AU - Basegmez du Pree, S.

AU - Becherini, Y.

AU - Bendahman, M.

AU - Benfenati, F.

AU - Benhassi, M.

AU - Benoit, D. M.

AU - Berbee, E.

AU - Bertin, V.

AU - Biagi, S.

AU - Boettcher, M.

AU - Bonanno, D.

AU - Boumaaza, J.

AU - Bouta, M.

AU - Bouwhuis, M.

AU - Bozza, C.

AU - Bozza, R. M.

AU - Brânzaş, H.

AU - Bretaudeau, F.

AU - Bruijn, R.

AU - Brunner, J.

AU - Bruno, R.

AU - Buis, E.

AU - Buompane, R.

AU - Busto, J.

AU - Caiffi, B.

AU - Calvo, D.

AU - Razzaque, S.

PY - 2024/9/27

Y1 - 2024/9/27

N2 - KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.

AB - KM3NeT is a research infrastructure hosting two large-volume Cherenkov neutrino detectors which are currently under construction in the Mediterranean Sea. The KM3NeT/ARCA detector is optimised for the detection of high-energy neutrinos from astrophysical sources in the TeV-PeV energy range. Once completed, the detector will consist of 230 detection units. Here, we present a Deep Learning method using graph neural networks that is trained and applied to events gathered with 6 and 8 active detection units of KM3NeT/ARCA. Graph neural networks have been trained for classification and regression tasks, showing very promising performances in a range of different tasks like neutrino-background identification, neutrino event topology classification, energy and direction reconstruction, and also in the study of properties of muon bundles.

UR - https://www.scopus.com/pages/publications/85212282863

M3 - Conference article

AN - SCOPUS:85212282863

SN - 1824-8039

VL - 444

JO - Proceedings of Science

JF - Proceedings of Science

M1 - 1194

T2 - 38th International Cosmic Ray Conference, ICRC 2023

Y2 - 26 July 2023 through 3 August 2023

ER -

Data reconstruction and classification with graph neural networks in KM3NeT/ARCA6-8

Abstract

ASJC Scopus subject areas

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