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

Ageron, M.; Aiello, S.; Ameli, F.; André, M.; Androulakis, G.; Anghinolfi, M.; Anton, Gisela; Ardid, M.; Aublin, J.; Bagatelas, C.; Barbarino, G. C.; Baret, B.; Pree, S. Başeğmez du; Belias, A.; Berbee, E.; Berg, A. M. van den; Bertín, V.; Beveren, V. van; Biagi, S.; Biagioni, A.; Bianucci, S.; Billault, M.; Bissinger, M.; Boer, Rizaldi; Boumaaza, J.; Bourret, S.; Bouta, M.; Bouwhuis, M. C.; Bozza, C.; Brânzaş, H.; Briel, M.; Bruchner, Marc; Bruijn, R.; Brünner, J.; Buis, E. J.; Buompane, R.; Busto, J.; Cacopardo, G.; Caillat, L.; Calı̀, C.; 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.; Colonges, S.; Coniglione, R.; Cosquer, A.; Coyle, P.; Creusot, A.; Cuttone, G.; D’Amato, Claudio; D’Amico, Antonio; D’Onofrio, Antonio; Dallier, R.; Palma, M. De; Sio, Chiara De; Palma, I. Di; Díaz, Antonio; Diego-Tortosa, D.; Distefano, C.; Domi, Alba; Donà, R.; Donzaud, C.; Dooren, L. van; Dornic, D.; Dörr, M.; Durocher, Mora; Eberl, T.; Eeden, T. van; Bojaddaini, I. El; Eljarrari, Hassnae; Elsäesser, D.; Enzenhöfer, A.; Fermani, P.; Ferrara, G.; Filipović, Miroslav; Fusco, L.; Gajanana, D.; Gal, T.; Garcia, A.; Garufi, F.; Gialanella, L.; Giorgio, Emidio; Giuliante, A.; Gozzini, S. R.; Gracía, R.; Graf, Κ.; Grasso, Dario; Grégoire, T.; Grella, G.; Grimaldi, A.; Grmek, A.; Guderian, D.; Guerzoni, M.; Guidi, C.; Hallmann, S.; Hamdaoui, H.; Haren, Hans van; Heijboer, A.; Hekalo, A.; Henry, S.; Hernández-Rey, J. J.; Hofestädt, J.; Huang, F.; Santiago, Enrique Huesca; Illuminati, G.; James, C. W.; Jansweijer, P.; Jongen, M.; Jong, M. de; Jong, P. de; Kadler, M.; Kalaczyński, P.; Kalekin, O.; Katz, U.; Kayzel, F.E.; Keller, P.; Khan-Chowdhury, N. R.; Knaap, F. van der; Koffeman, E.; Kooijman, P.; Koopstra, J.; Kouchner, A.; Kreter, M.; Kulikovskiy, V.; Meghna, K K; Lahmann, R.; Lamare, P.; Larosa, G.; Laurence, Jerome M.; Breton, R. Le; Leone, F.; Leonora, E.; Levi, G.; Librizzi, F.; Lincetto, Massimiliano; Litrico, P.; Alvarez, C. D. Llorens; Lonardo, A.; Longhitano, F.; Lopez-Coto, D.; 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.; Mongelli, M.; Morganti, M.; Möser, Michael; Moussa, A.; Müller, R.; Musico, P.; Musumeci, M.; Nauta, L.; Navas, S.; Nicolau, C. A.; Nielsen, C.; Fearraigh, B. Ó; Organokov, M.; Orlando, A.; Panagopoulos, V.; Pancaldi, Giuliano; Papalashvili, G.; Papaleo, R.; Pastore, C.; Păvălaş, G. E.; Pellegrini, G.; 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.; Rapicavoli, Antonio; Razzaque, S.; Real, D.; Reck, S.; Reubelt, J.; Riccobene, G.; Rigalleau, L.; Rizza, G.; Rocco, R.; Rovelli, A.; Royon, Jérôme; Salemi, M.; Salvadori, I.; Samtleben, D. F. E.; Sánchez-Losa, A.; Sanguineti, M.; Santangelo, A.; Santonocito, D.; Sapienza, P.; Schmelling, Jan-Willem; Sciacca, Virginia; Sciliberto, D.; Seneca, J.; Sgura, I.; Shanidze, R.; Sharma, A.; Simeone, F.; Sinopoulou, A.; Spisso, B.; Spurio, M.; Stavropoulos, D.; Steijger, J. J. M.; Stellacci, Francesco; Strandberg, B.; Stransky, D.; Stüven, T.; Taiuti, M.; Tayalati, Y.; Tenllado, E.; Tézier, D.; Thakore, T.; Theraube, Stephane; Timmer, P.; Tingay, S. J.; Trovato, A.; Tsagkli, S.; Tzamariudaki, E.; Tzanetatos, D.; Valieri, C.; Elewyck, V. Van; Versari, F.; Viola, S.; Vivolo, D.; Wasseige, Gwenhaël de; Wilms, J.; Wojaczyński, R.; Wolf, E. de; Zaborov, D.; Zegarelli, A.; Zornoza, J. D.; Zúñiga, J.

doi:10.1140/epjc/s10052-020-7629-z

Cited by 35 publications

(31 citation statements)

References 19 publications

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“…The good level of agreement confirms the reliability of the simulation procedure defined so far within KM3NeT. Sharing the same technology and detection medium (sea water), the ARCA and ORCA detectors offer a unique opportunity to probe the depth dependence of the atmospheric muon flux without applying systematic uncertainties related to comparison between different types of experiments [8].…”

Section: Discussionsupporting

confidence: 68%

“…This is done with the addition of the environmental optical background, due to the bioluminescence and to the 40 K decay, with the simulation of the detector response, taking into account the front-end electronics behaviour. The same trigger algorithms used for real data are used to identify possible interesting events in the simulated sample [8]. Finally, the simulated events are processed with the same reconstruction programs used for the real data stream.…”

Section: Simulation Chainmentioning

confidence: 99%

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Comparison of the measured atmospheric muon flux with Monte Carlo simulations for the first KM3NeT detection units

Kalaczyński¹,

Coniglione²

2019

Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019)

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The KM3NeT Collaboration has successfully deployed its first detection units in the Mediterranean Sea in December 2015 (ARCA) and September 2017 (ORCA). The sample of data collected between September 2016 and March 2017 has been used to measure the atmospheric muon flux at two different depths under the sea level: about 3.5 km with ARCA and about 2.5 km with ORCA. The atmospheric muon flux represents an abundant signal for a neutrino telescope and can be used to test the reliability of the Monte Carlo simulation chain. In this work, the measurements are compared to Monte Carlo simulations based on MUPAGE and CORSIKA codes. Measured events and simulated events are treated using the same approach, making the comparison reliable.

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Section: Discussionsupporting

confidence: 68%

Section: Simulation Chainmentioning

confidence: 99%

Comparison of the measured atmospheric muon flux with Monte Carlo simulations for the first KM3NeT detection units

Kalaczyński¹,

Coniglione²

2019

Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019)

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“…For a KM3NeT DOM, the background rate as a function of the multiplicity is characterised by the distribution presented in Ref. [26]. Radioactive decays dominate at low multiplicities, with a rate of ∼ 500 Hz at multiplicity 2, roughly decreasing by an order of magnitude for every step in multiplicity.…”

Section: Optical Background Measurementmentioning

confidence: 99%

The KM3NeT potential for the next core-collapse supernova observation with neutrinos

et al. 2021

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The KM3NeT research infrastructure is under construction in the Mediterranean Sea. It consists of two water Cherenkov neutrino detectors, ARCA and ORCA, aimed at neutrino astrophysics and oscillation research, respectively. Instrumenting a large volume of sea water with $$\sim {6200}$$ ∼ 6200 optical modules comprising a total of $$\sim {200{,}000}$$ ∼ 200 , 000 photomultiplier tubes, KM3NeT will achieve sensitivity to $$\sim {10} \ \mathrm{MeV}$$ ∼ 10 MeV neutrinos from Galactic and near-Galactic core-collapse supernovae through the observation of coincident hits in photomultipliers above the background. In this paper, the sensitivity of KM3NeT to a supernova explosion is estimated from detailed analyses of background data from the first KM3NeT detection units and simulations of the neutrino signal. The KM3NeT observational horizon (for a $$5\,\sigma $$ 5 σ discovery) covers essentially the Milky-Way and for the most optimistic model, extends to the Small Magellanic Cloud ($$\sim {60} \ \mathrm{kpc}$$ ∼ 60 kpc ). Detailed studies of the time profile of the neutrino signal allow assessment of the KM3NeT capability to determine the arrival time of the neutrino burst with a few milliseconds precision for sources up to 5–8 kpc away, and detecting the peculiar signature of the standing accretion shock instability if the core-collapse supernova explosion happens closer than 3–5 kpc, depending on the progenitor mass. KM3NeT’s capability to measure the neutrino flux spectral parameters is also presented.

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“…Each TDC channel has to be able to deal with pulse rates up to 200 kHz, with 7 kHz being the expected average rate of the PMT signals. 13 Table 1 summarizes the data throughput expected for different stages of the KM3NeT infrastructure. The TDCs acquire the LVDS signals generated by the PMTs base, detecting its rise time and duration (ToT) with 1 ns precision.…”

Section: Tdc Requirementsmentioning

confidence: 99%

Architecture and performance of the KM3NeT front-end firmware

Aiello

Albert

Garre

et al. 2021

J. Astron. Telesc. Instrum. Syst.

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The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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

Cited by 35 publications

References 19 publications

Comparison of the measured atmospheric muon flux with Monte Carlo simulations for the first KM3NeT detection units

Comparison of the measured atmospheric muon flux with Monte Carlo simulations for the first KM3NeT detection units

The KM3NeT potential for the next core-collapse supernova observation with neutrinos

Architecture and performance of the KM3NeT front-end firmware

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