Improving the sensitivity of KM3NeT to MeV-GeV neutrinos from solar flares

KM3NeT Collaboration

Improving the sensitivity of KM3NeT to MeV-GeV neutrinos from solar flares

KM3NeT Collaboration

Physics

Université catholique de Louvain
National Institute for Nuclear Physics
Université de Strasbourg
Université de Haute-Alsace
University of Valencia
Aix-Marseille Université
University of Naples Federico II
Polytechnic University of Catalonia
Demokritos National Centre for Scientific Research
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 Bologna
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
Friedrich-Alexander University Erlangen-Nürnberg
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
Leiden University
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
Université Montpellier 2

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

Abstract

The detection of MeV-GeV neutrinos from astronomical sources is a long-lasting challenge for neutrino experiments. The low flux predicted for transient sources, such as solar flares, and their low-energy signature, requires a detector with both a large instrumented volume as well as a high density of photomultiplier tubes (PMTs). We discuss how KM3NeT can play a key role in the search for these neutrinos. KM3NeT is a Cherenkov neutrino telescope currently under deployment, located at the bottom of the Mediterranean Sea. It consists of two arrays of Digital Optical Modules (DOMs): KM3NeT/ORCA and KM3NeT/ARCA, which are optimised for the detection of GeV neutrinos for oscillation studies, and higher-energy astronomical neutrinos respectively. We exploit the multi-PMT configuration of KM3NeT’s DOMs to develop the techniques that allow the disentangling of the MeV-GeV neutrino signature from the atmospheric and environmental background. Comparing data with neutrino simulations we identify the variables with discriminating power, and by applying hard cuts we are able to reject a large fraction of background. We present a graph neural network approach to classify signal from background. To further improve the sensitivities compared to previous studies, we will make use of the Hierarchical Graph Pooling with Structure Learning algorithm and will use graph-structured data to reproduce the hit geometry on the DOM. This will allow for stronger constraints on the hits and reduce the fraction of background that survives the selection.

Original language	English
Article number	1294
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{76e2f7603b8e4f58ac98115385c51ea8,

title = "Improving the sensitivity of KM3NeT to MeV-GeV neutrinos from solar flares",

abstract = "The detection of MeV-GeV neutrinos from astronomical sources is a long-lasting challenge for neutrino experiments. The low flux predicted for transient sources, such as solar flares, and their low-energy signature, requires a detector with both a large instrumented volume as well as a high density of photomultiplier tubes (PMTs). We discuss how KM3NeT can play a key role in the search for these neutrinos. KM3NeT is a Cherenkov neutrino telescope currently under deployment, located at the bottom of the Mediterranean Sea. It consists of two arrays of Digital Optical Modules (DOMs): KM3NeT/ORCA and KM3NeT/ARCA, which are optimised for the detection of GeV neutrinos for oscillation studies, and higher-energy astronomical neutrinos respectively. We exploit the multi-PMT configuration of KM3NeT{\textquoteright}s DOMs to develop the techniques that allow the disentangling of the MeV-GeV neutrino signature from the atmospheric and environmental background. Comparing data with neutrino simulations we identify the variables with discriminating power, and by applying hard cuts we are able to reject a large fraction of background. We present a graph neural network approach to classify signal from background. To further improve the sensitivities compared to previous studies, we will make use of the Hierarchical Graph Pooling with Structure Learning algorithm and will use graph-structured data to reproduce the hit geometry on the DOM. This will allow for stronger constraints on the hits and reduce the fraction of background that survives the selection.",

author = "\{KM3NeT Collaboration\} and Jonathan Mauro and \{de Wasseige\}, Gwenha{\"e}l 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. Campion and A. Capone and F. Carenini 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 - Improving the sensitivity of KM3NeT to MeV-GeV neutrinos from solar flares

AU - KM3NeT Collaboration

AU - Mauro, Jonathan

AU - de Wasseige, Gwenhaël

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 - Campion, S.

AU - Capone, A.

AU - Carenini, F.

AU - Razzaque, S.

PY - 2024/9/27

Y1 - 2024/9/27

N2 - The detection of MeV-GeV neutrinos from astronomical sources is a long-lasting challenge for neutrino experiments. The low flux predicted for transient sources, such as solar flares, and their low-energy signature, requires a detector with both a large instrumented volume as well as a high density of photomultiplier tubes (PMTs). We discuss how KM3NeT can play a key role in the search for these neutrinos. KM3NeT is a Cherenkov neutrino telescope currently under deployment, located at the bottom of the Mediterranean Sea. It consists of two arrays of Digital Optical Modules (DOMs): KM3NeT/ORCA and KM3NeT/ARCA, which are optimised for the detection of GeV neutrinos for oscillation studies, and higher-energy astronomical neutrinos respectively. We exploit the multi-PMT configuration of KM3NeT’s DOMs to develop the techniques that allow the disentangling of the MeV-GeV neutrino signature from the atmospheric and environmental background. Comparing data with neutrino simulations we identify the variables with discriminating power, and by applying hard cuts we are able to reject a large fraction of background. We present a graph neural network approach to classify signal from background. To further improve the sensitivities compared to previous studies, we will make use of the Hierarchical Graph Pooling with Structure Learning algorithm and will use graph-structured data to reproduce the hit geometry on the DOM. This will allow for stronger constraints on the hits and reduce the fraction of background that survives the selection.

AB - The detection of MeV-GeV neutrinos from astronomical sources is a long-lasting challenge for neutrino experiments. The low flux predicted for transient sources, such as solar flares, and their low-energy signature, requires a detector with both a large instrumented volume as well as a high density of photomultiplier tubes (PMTs). We discuss how KM3NeT can play a key role in the search for these neutrinos. KM3NeT is a Cherenkov neutrino telescope currently under deployment, located at the bottom of the Mediterranean Sea. It consists of two arrays of Digital Optical Modules (DOMs): KM3NeT/ORCA and KM3NeT/ARCA, which are optimised for the detection of GeV neutrinos for oscillation studies, and higher-energy astronomical neutrinos respectively. We exploit the multi-PMT configuration of KM3NeT’s DOMs to develop the techniques that allow the disentangling of the MeV-GeV neutrino signature from the atmospheric and environmental background. Comparing data with neutrino simulations we identify the variables with discriminating power, and by applying hard cuts we are able to reject a large fraction of background. We present a graph neural network approach to classify signal from background. To further improve the sensitivities compared to previous studies, we will make use of the Hierarchical Graph Pooling with Structure Learning algorithm and will use graph-structured data to reproduce the hit geometry on the DOM. This will allow for stronger constraints on the hits and reduce the fraction of background that survives the selection.

UR - https://www.scopus.com/pages/publications/85212258673

M3 - Conference article

AN - SCOPUS:85212258673

SN - 1824-8039

VL - 444

JO - Proceedings of Science

JF - Proceedings of Science

M1 - 1294

T2 - 38th International Cosmic Ray Conference, ICRC 2023

Y2 - 26 July 2023 through 3 August 2023

ER -

Improving the sensitivity of KM3NeT to MeV-GeV neutrinos from solar flares

Abstract

ASJC Scopus subject areas

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