Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources

Aiello, S.; Akrame, S.E.; Ameli, F.; Anassontzis, E.G.; André, M.; Androulakis, G.; Anghinolfi, M.; Anton, G.; Ardid, M.; Aublin, J.; Avgitas, T.; Bagatelas, C.; Barbarino, G. C.; Baret, B.; Barrios-Martí, J.; Belias, A.; Berbee, E.; Berg, Albert van den; Bertín, V.; Biagi, S.; Biagioni, A.; Biernoth, C.; Boumaaza, J.; Bourret, S.; Bouta, M.; Bouwhuis, M. C.; Bozza, C.; Brânzaş, H.; Bruchner, Marc; Bruijn, R.; Brünner, J.; Buis, E. J.; Buompane, R.; Busto, J.; Calvo, D.; Capone, A.; Celli, S.; Chabab, M.; Chau, N.; Cherubini, S.; Chiarella, V.; Chiarusi, T.; Circella, M.; Cocimano, R.; Coelho, J. A. B.; Coleiro, A.; Colomer-Molla, M.; Coniglione, R.; Coyle, P.; Creusot, A.; Cuttone, G.; D’onofrio, A.; Dallier, R.; Sio, Chiara De; Palma, I. Di; Díaz, Antonio; Diego-Tortosa, D.; Distefano, C.; Domi, Alba; Donà, R.; Donzaud, C.; Dornic, D.; Dörr, M.; Durocher, Mora; Eberl, T.; Eijk, D. van; Bojaddaini, I. El; Eljarrari, Hassnae; Elsäesser, D.; Enzenhöfer, A.; Fermani, P.; Ferrara, G.; Filipović, Miroslav; Fusco, L.; Gal, T.; García, Alfonso; Garufi, F.; Gialanella, L.; Giorgio, Emidio; Giuliante, A.; Gozzini, S. R.; Gracía, R.; Graf, Κ.; Grasso, Dario; Grégoire, T.; Grella, G.; Hallmann, S.; Hamdaoui, H.; Haren, Hans van; Heid, T.; Heijboer, A.; Hekalo, A.; Hernández-Rey, J. J.; Hofestädt, J.; Illuminati, G.; James, C. W.; Jongen, M.; Jong, M. de; Jong, P. de; Kadler, M.; Kalaczyński, P.; Kalekin, O.; Katz, U.; Khan-Chowdhury, N. R.; Kießling, D.; Koffeman, E.; Kooijman, P.; Kouchner, A.; Kreter, M.; Kulikovskiy, V.; Kannichankandy, M. Kunhikannan; Lahmann, R.; Larosa, G.; Breton, R. Le; Leone, F.; Leonora, E.; Levi, G.; Lincetto, Massimiliano; Lonardo, A.; Longhitano, F.; Coto, Daniel López; Lotze, Martín; Maderer, L.; Maggi, G.; Mańczak, J.; Mannheim, K.; Margiotta, A.; Marinelli, A.; Markou, C.; Martin, L.; Martínez-Mora, J. A.; Martini, A.; Marzaioli, Fabio; Mele, R.; Melis, K.; Migliozzi, P.; Migneco, E.; Mijakowski, P.; Miranda, Luis Salvador; Mollo, C.M.; Morganti, M.; Möser, Michael; Moussa, A.; Müller, R.; Musumeci, M.; Nauta, L.; Navas, S.; Nicolau, C. A.; Nielsen, C.; Fearraigh, B. Ó; Organokov, M.; Orlando, A.; Ottonello, Stefano; Panagopoulos, V.; Papalashvili, G.; Papaleo, R.; Păvălaş, G. E.; Pellegrino, C.; Perrin-Terrin, M.; Piattelli, P.; Pikounis, K.; Pisanti, O.; Poirè, C.; Polydefki, G.; Popa, V.; Post, M.; Pradier, T.; Pühlhofer, G.; Pulvirenti, S.; Quinn, L.; Raffaelli, F.; Randazzo, N.; Razzaque, S.; Real, D.; Resvanis, L. K.; Reubelt, J.; Riccobene, G.; Richer, M.; Rigalleau, L.; Rovelli, A.; Saffer, M.; Salvadori, I.; Samtleben, D. F. E.; Sánchez-Losa, A.; Sanguineti, M.; Santangelo, A.; Santonocito, D.; Sapienza, P.; Schümann, J.; Sciacca, Virginia; Seneca, J.; Sgura, I.; Shanidze, R.; Sharma, Ankur; Simeone, F.; Sinopoulou, A.; Spisso, B.; Spurio, M.; Stavropoulos, D.; Steijger, J. J. M.; Stellacci, Francesco; Strandberg, B.; Stransky, D.; Stüven, T.; Taiuti, M.; Tatone, Francesca; Tayalati, Y.; Tenllado, E.; Thakore, T.; Trovato, A.; Tzamariudaki, E.; Tzanetatos, D.; Elewyck, V. Van; Versari, F.; Viola, S.; Vivolo, D.; Wilms, J.; Wolf, E. de; Zaborov, D.; Zornoza, J.D.; Zúñiga, J.

doi:10.1016/j.astropartphys.2019.04.002

Cited by 106 publications

(72 citation statements)

References 84 publications

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“…pLISA does not use any preprocessed information. In order to develop the software and a library of networks [4,5], the KM3NeT/ARCA detector (for Astroparticle Research with Cosmics in the Abyss) [6,7] was used as a reference and events from standard simulations were considered. The training and validation data samples were made of mixed νμ and νe chargedcurrent interactions in sea water.…”

Section: Application To Simulated Eventsmentioning

confidence: 99%

pLISA: a parallel Library for Identification and Study of Astroparticles

Bozza¹,

Sio²,

Coniglione³

2019

Proceedings of the New Era of Multi-Messenger Astrophysics — PoS(Asterics2019)

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INFN has produced a Machine Learning library in Python that applies Convolutional Neural Networks to various common problems in the field of astroparticle identification and study in suitable detectors. The library itself makes few assumptions and has few requirements that are easily met in most astroparticle detectors. The Parallel Library for Identification and Study of Astroparticles (pLISA) has been tested against simulated events for the KM3NeT/ARCA detector. Interesting preliminary results have been obtained for up/down-going particle classification, muon/electron neutrino classification, Z component of the direction and energy estimation. Already with very little optimization work and using limited hardware resources (one NVidia GTX GPU), pLISA was shown to compete with traditional algorithms. The approach allows improvements and also portability to other detectors. pLISA is based on commonly used open source frameworks, which helps ensuring portability and scalability.

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Section: Application To Simulated Eventsmentioning

confidence: 99%

pLISA: a parallel Library for Identification and Study of Astroparticles

Bozza¹,

Sio²,

Coniglione³

2019

Proceedings of the New Era of Multi-Messenger Astrophysics — PoS(Asterics2019)

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“…The ARCA (Astroparticle Research with Cosmics in the Abyss) detector is being installed at the KM3NeT-It site, 80 km offshore the Sicilian coast in front of Capo Passero (Italy) at a sea bottom depth of about 3450 m. The main physics goal of ARCA is the discovery and subsequent observation of astrophysical neutrino sources in the Universe [2]. About 1 km 3 of seawater will be instrumented with ∼ 130,000 photomultiplier tubes (PMTs) for the detection of Cherenkov light induced by charged particles produced in neutrino interactions.…”

Section: Introductionmentioning

confidence: 99%

Dependence of atmospheric muon flux on seawater depth measured with the first KM3NeT detection units

et al. 2020

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KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained.

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“…With 10 years of data taking and and no indication of a steady neutrino point source, its detection will be challenging with the current detector geometry as the discovery potential increases as a squared root of the acquisition time. In the long term, high-energy extensions of neutrino detectors are in preparation like the IceCube-Gen2 detector [65,66] and KM3NeT [67,68] to enhance the statistics and thus the sensitivity to astrophysical neutrino sources. In addition, also an improved understanding of the detector and thus a reduction of systematic uncertainties, e.g., by the IceCube-Upgrade [69], will help to improve the sensitivity of the already existing data, by re-analyzing them with better knowledge of the systematic uncertainties.…”

Section: Discussionmentioning

confidence: 99%

Monitoring and Multi-Messenger Astronomy with IceCube

Reimann

2019

Galaxies

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IceCube currently is the largest neutrino observatory with an instrumented detection volume of 1 km3 buried in the ice-sheet close to the antarctic South Pole station. With a 4 π field of view and an up-time of >99%, it is continuously monitoring the full sky to detect astrophysical neutrinos. With the detection of an astrophysical neutrino flux in 2013, IceCube opened a new observation window to the non-thermal Universe. The IceCube collaboration has a large program to search for astrophysical neutrinos, including measurements of the energy spectrum of the diffuse astrophysical flux, auto- and cross-correlation studies with other multi-messenger particles, and a real-time alert and follow-up system. On 22 September 2017, the IceCube online system sent out an alert reporting a high-energy neutrino event. This alert triggered a series of multi-wavelength follow-up observations that revealed a spatially-coincident blazar TXS 0506+056, which was also in an active flaring state. This correlation was estimated at a 3 σ level. Further observations confirmed the flaring emission in the very-high-energy gamma-ray band. In addition, IceCube found an independent 3.5 σ excess of a time-variable neutrino flux in the direction of TXS 0506+056 two years prior to the alert by examining 9.5 years of archival neutrino data. These are the first multi-messenger observations of an extra-galactic astrophysical source including neutrinos since the observation of the supernova SN1987A. This review summarizes the different detection and analysis channels for astrophysical neutrinos in IceCube, focusing on the multi-messenger program of IceCube and its major scientific results.

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Sensitivity of the KM3NeT/ARCA neutrino telescope to point-like neutrino sources

Cited by 106 publications

References 84 publications

pLISA: a parallel Library for Identification and Study of Astroparticles

pLISA: a parallel Library for Identification and Study of Astroparticles

Dependence of atmospheric muon flux on seawater depth measured with the first KM3NeT detection units

Monitoring and Multi-Messenger Astronomy with IceCube

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