Search for magnetic monopoles with ten years of the ANTARES neutrino telescope

This work presents a new search for magnetic monopoles using data taken with the ANTARES neutrino telescope over a period of 10 years (January 2008 to December 2017). Compared to previous ANTARES searches, this analysis uses a run-by-run simulation strategy, with a larger exposure as well as a new simulation of magnetic monopoles taking into account the Kasama, Yang and Goldhaber model for their interaction cross-section with matter. No signal compatible with the passage of relativistic magnetic monopoles is observed, and upper limits on the flux of magnetic monopoles with ß = v/c = 0.55, are presented. For ultra-relativistic magnetic monopoles the flux limit is ~ 7×10-18 cm-2 s -1 sr-1 .Postprint (author's final draft

Search for an association between neutrinos and radio-selected blazars with ANTARES

Recently, evidence for an association between high energy neutrinos detected by IceCube and radio-selected blazars has been found by Plavin et al.(2020, 2021). This result wa.s achieved using an all sky complete sample of 3411 blazars selected on their parsec-scale flux density at 8 GHz higher than 150 mJy. We perform a positional correlation analysis using the same sample of radioselected blazars, with the latest point source sample of neutrinos extracted from the data collected by the ANTARES detector between January 29, 2007 and February 28, 2020. Preliminary results are presented and discussedPostprint (published version

Search for non-standard neutrino interactions with 10 years of ANTARES data

Non-standard interactions of neutrinos arising in many theories beyond the Standard Model can significantly alter matter effects in atmospheric neutrino propagation through the Earth. In this paper, a search for deviations from the prediction of the standard 3-flavour atmospheric neutrino oscillations using the data taken by the ANTARES neutrino telescope is presented. Ten years of atmospheric neutrino data collected from 2007 to 2016, with reconstructed energies in the range from ~16 GeV to 100 GeV, have been analysed. A log-likelihood ratio test of the dimensionless coefficients eµt and ett-eµµ does not provide clear evidence of deviations from standard interactions. For normal neutrino mass ordering, the combined fit of both coefficients yields a value 1.7s away from the null result. However, the 68% and 95% confidence level intervals for eµt and ett-eµµ, respectively, contain the null value. Best fit values, one standard deviation errors and bounds at the 90% confidence level for these coefficients are given for both normal and inverted mass orderings. The constraint on eµt is among the most stringent to date and it further restrains the strength of possible non-standard interactions in the µ-t sector.Postprint (published version

Hint for a TeV neutrino emission from the Galactic Ridge with ANTARES

Interactions of cosmic ray protons, atomic nuclei, and electrons in the interstellar medium in the inner part of the Milky Way produce a ¿-ray flux from the Galactic Ridge. If the ¿-ray emission is dominated by proton and nuclei interactions, a neutrino flux comparable to the ¿-ray flux is expected from the same sky region. Data collected by the ANTARES neutrino telescope are used to constrain the neutrino flux from the Galactic Ridge in the 1-100 TeV energy range. Neutrino events reconstructed both as tracks and showers are considered in the analysis and the selection is optimized for the search of an excess in the region |l| <30¿, |b| <2¿. The expected background in the search region is estimated using an off-zone region with similar sky coverage. Neutrino signal originating from a power-law spectrum with spectral index ranging from ¿=1to 4is simulated in both channels. The observed energy distributions are fitted to constrain the neutrino emission from the Ridge. The energy distributions in the signal region are inconsistent with the background expectation at ~96% confidence level. The mild excess over the background is consistent with a neutrino flux with a power law with a spectral index 2.45+0.22 -0.34and a flux normalization dN¿ dE¿ =4.0+2.7 -2.0 ×10-16GeV-1cm-2s-1sr-1 at 40 TeV reference energy. Such flux is consistent with the expected neutrino signal if the bulk of the observed ¿-ray flux from the Galactic Ridge originates from interactions of cosmic ray protons and nuclei with a power-law spectrum extending well into the PeV energy range.The authors acknowledge the financial support of the funding agencies: Centre National de la Recherche Scientifique (CNRS), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), Commission Européenne (FEDER fund and Marie Curie Program), Labex UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Région Alsace (contrat CPER), Région Provence-Alpes-Côte d'Azur, Département du Var and Ville de La Seyne-sur-Mer, France; Bundesministerium für Bildung und Forschung (BMBF), Germany; Istituto Nazionale di Fisica Nucleare (INFN), the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 754496, Italy; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI), Romania; Grants PID2021-124591NB-C41, -C42, -C43 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”, Programa de Planes Complementarios I+D+I (refs. ASFAE/2022/023, ASFAE/2022/014), Programa Prometeo (PROMETEO/2020/019) and GenT (refs. CIDEGENT/2018/034, /2019/043, /2020/049. /2021/23) of the Generalitat Valenciana, Junta de Andalucía (ref. P18-FR-5057), EU: MSC program (ref. 101025085), Programa María Zambrano (Spanish Ministry of Universities, funded by the European Union, NextGenerationEU), Spain; Ministry of Higher Education, Scientific Research and Training, Morocco, and the Arab Fund for Economic and Social Development, Kuwait. We also acknowledge the technical support of Ifremer, AIM and Foselev Marine for the sea operation and the CC-IN2P3 for the computing facilities.Peer ReviewedArticle signat per 164 autors/es: A. Albert, S. Alves, M. André, M. Ardid , S. Ardid, J.-J. Aubert, J. Aublin, B. Baret, S. Basa, Y. Becherini, B. Belhorma, M. Bendahman, F. Benfenati, V. Bertin, S. Biagi, M. Bissinger, J. Boumaaza, M. Bouta, M.C. Bouwhuis, H. Brânzaş, R. Bruijn, J. Brunner, J. Busto, B. Caiffi, D. Calvo, S. Campion, A. Capone, L. Caramete, F. Carenini, J. Carr, V. Carretero, S. Celli, L. Cerisy, M. Chabab, T.N. Chau, R. Cherkaoui El Moursli, T. Chiarusi, M. Circella, J.A.B. Coelho, A. Coleiro, R. Coniglione, P. Coyle, A. Creusot, A.F. Díaz, B. De Martino, C. Distefano, I. Di Palma, A. Domi , C. Donzaud, D. Dornic, D. Drouhin, T. Eberl, T. van Eeden, D. van Eijk, S. El Hedri, N. El Khayati, A. Enzenhöfer, M. Fasano, P. Fermani, G. Ferrara, F. Filippini, L. Fusco, S. Gagliardini, J. García, C. Gatius Oliver, P. Gay, N. Geißelbrecht, H. Glotin, R. Gozzini, R. Gracia Ruiz, K. Graf, C. Guidi, L. Haegel, S. Hallmann, H. van Haren, A.J. Heijboer, Y. Hello, J.J. Hernández-Rey, J. Hößl, J. Hofestädt, F. Huang, G. Illuminati, C.W. James, B. Jisse-Jung, M. de Jong, P. de Jong, M. Kadler, O. Kalekin, U. Katz, A. Kouchner, I. Kreykenbohm, V. Kulikovskiy, R. Lahmann, M. Lamoureux, A. Lazo, D. Lefèvre, E. Leonora, G. Levi, S. Le Stum, D. Lopez-Coto, S. Loucatos, L. Maderer, J. Manczak, M. Marcelin, A. Margiotta, A. Marinelli, J.A. Martínez-Mora, P. Migliozzi, A. Moussa, R. Muller, L. Nauta, S. Navas, A. Neronov, E. Nezri, B. Ó Fearraigh, A. Păun, G.E. Păvălaş, M. Perrin-Terrin, V. Pestel, P. Piattelli, C. Poirè, V. Popa, T. Pradier, N. Randazzo, D. Real, S. Reck, G. Riccobene, A. Romanov, A. Sánchez-Losa, A. Saina, F. Salesa Greus, D.F.E. Samtleben, M. Sanguineti, P. Sapienza, D. Savchenko, J. Schnabel, J. Schumann, F. Schüssler, J. Seneca, M. Spurio, Th. Stolarczyk, M. Taiuti, Y. Tayalati, S.J. Tingay, B. Vallage, G. Vannoye, V. Van Elewyck, S. Viola, D. Vivolo, J. Wilms, S. Zavatarelli, A. Zegarelli, J.D. Zornoza, J. Zúñiga.Postprint (published version

Bounding the Time Delay between High-energy Neutrinos and Gravitational-wave Transients from Gamma-ray Bursts

We derive a conservative coincidence time window for joint searches of gravita-tional-wave (GW) transients and high-energy neutrinos (HENs, with energies above 100GeV), emitted by gamma-ray bursts (GRBs). The last are among the most interesting astrophysical sources for coincident detections with current and near-future detectors. We take into account a broad range of emission mechanisms. We take the upper limit of GRB durations as the 95% quantile of the T90's of GRBs observed by BATSE, obtaining a GRB duration upper limit of ~150s. Using published results on high-energy (>100MeV) photon light curves for 8 GRBs detected by Fermi LAT, we verify that most high-energy photons are expected to be observed within the first ~150s of the GRB. Taking into account the breakout-time of the relativistic jet produced by the central engine, we allow GW and HEN emission to begin up to 100s before the onset of observable gamma photon production. Using published precursor time differences, we calculate a time upper bound for precursor activity, obtaining that 95% of precursors occur within ~250s prior to the onset of the GRB. Taking the above different processes into account, we arrive at a time window of tHEN - tGW ~ [-500s,+500s]. Considering the above processes, an upper bound can also be determined for the expected time window of GW and/or HEN signals coincident with a detected GRB, tGW - tGRB ~ tHEN - tGRB ~ [-350s,+150s]

Architecture and performance of the KM3NeT front-end firmware

[EN] 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 machineThe authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.Aiello, S.; Albert, A.; Alves Garre, S.; Aly, Z.; Ameli, F.; Andre, M.; Androulakis, G.... (2021). Architecture and performance of the KM3NeT front-end firmware. Journal of Astronomical Telescopes, Instruments, and Systems. 7(1):1-24. https://doi.org/10.1117/1.JATIS.7.1.016001S1247

Architecture and performance of the KM3NeT front-end firmware

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

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches