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ANTARES Collaboration(Albert, A. et al), Alves, S., Calvo, D., Carretero, V., Gozzini, R., Hernandez-Rey, J. J., et al. (2023). Review of the online analyses of multi-messenger alerts and electromagnetic transient events with the ANTARES neutrino telescope. J. Cosmol. Astropart. Phys., 08(8), 072–23pp.
Abstract: By constantly monitoring a very large portion of the sky, neutrino telescopes are well-designed to detect neutrinos emitted by transient astrophysical events. Real-time searches with the ANTARES telescope have been performed to look for neutrino candidates coincident with gamma-ray bursts detected by the Swift and Fermi satellites, high-energy neutrino events registered by IceCube, transient events from blazars monitored by HAWC, photon-neutrino coincidences by AMON notices and gravitational wave candidates observed by LIGO/Virgo. By requiring temporal coincidence, this approach increases the sensitivity and the significance of a potential discovery. This paper summarises the results of the followup performed of the ANTARES telescope between January 2014 and February 2022, which corresponds to the end of the data-taking period.
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ANTARES Collaboration(Albert, A. et al), Alves, S., Calvo, D., Carretero, V., Gozzini, R., Hernandez-Rey, J. J., et al. (2022). Search for solar atmospheric neutrinos with the ANTARES neutrino telescope. J. Cosmol. Astropart. Phys., 06(6), 018–17pp.
Abstract: Solar Atmospheric Neutrinos (SA nu s) are produced by the interaction of cosmic rays with the solar medium. The detection of SA nu s would provide useful information on the composition of primary cosmic rays as well as the solar density. These neutrinos represent an irreducible source of background for indirect searches for dark matter towards the Sun and the measurement of their flux would allow for a better assessment of the uncertainties related to these searches. In this paper we report on the analysis performed, based on an unbinned likelihood maximisation, to search for SA nu s with the ANTARES neutrino telescope. After analysing the data collected over 11 years, no evidence for a solar atmospheric neutrino signal has been found. An upper limit at 90% confidence level on the flux of solar atmospheric neutrinos has been obtained, equal to 7x10(-11) [TeV-1 cm(-2) s(-1)] b at E-nu = 1 TeV for the reference cosmic ray model assumed.
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ANTARES Collaboration(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2021). Monte Carlo simulations for the ANTARES underwater neutrino telescope. J. Cosmol. Astropart. Phys., 01(1), 064–20pp.
Abstract: Monte Carlo simulations are a unique tool to check the response of a detector and to monitor its performance. For a deep-sea neutrino telescope, the variability of the environmental conditions that can affect the behaviour of the data acquisition system must be considered, in addition to a reliable description of the active parts of the detector and of the features of physics events, in order to produce a realistic set of simulated events. In this paper, the software tools used to produce neutrino and cosmic ray signatures in the telescope and the strategy developed to represent the time evolution of the natural environment and of the detector efficiency are described.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo, D., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2016). A method to stabilise the performance of negatively fed KM3NeT photomultipliers. J. Instrum., 11, P12014–12pp.
Abstract: The KM3NeT research infrastructure, currently under construction in the Mediterranean Sea, will host neutrino telescopes for the identification of neutrino sources in the Universe and for studies of the neutrino mass hierarchy. These telescopes will house hundreds of thousands of photomultiplier tubes that will have to be operated in a stable and reliable fashion. In this context, the stability of the dark counts has been investigated for photomultiplier tubes with negative high voltage on the photocathode and held in insulating support structures made of 3D printed nylon material. Small gaps between the rigid support structure and the photomultiplier tubes in the presence of electric fields can lead to discharges that produce dark count rates that are highly variable. A solution was found by applying the same insulating varnish as used for the high voltage bases directly to the outside of the photomultiplier tubes. This transparent conformal coating provides a convenient and inexpensive method of insulation.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: 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.
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