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.
N1 - Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons.
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 - http://www.scopus.com/inward/record.url?scp=85212282863&partnerID=8YFLogxK
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 -