Abstract: On 2019 October 1, the IceCube Collaboration detected a muon track neutrino with a high probability of being of astrophysical origin, IC191001A. After a few hours, the tidal disruption event (TDE) AT2019dsg, observed by the Zwicky Transient Facility (ZTF), was indicated as the most likely counterpart of the IceCube track. More recently, the follow-up campaign of the IceCube alerts by ZTF suggested a second TDE, AT2019fdr, as a promising counterpart of another IceCube muon track candidate, IC200530A, detected on 2020 May 30. Here, these intriguing associations are followed-up by searching for neutrinos in the ANTARES detector from the directions of AT2019dsg and AT2019fdr using a time-integrated approach. As no significant evidence for space clustering is found in the ANTARES data, upper limits on the one-flavor neutrino flux and fluence are set.

Abstract: In this work, a search for nuclearites of strange quark matter by using nine years of ANTARES data taken in the period 2009-2017 is presented. The passage through matter of these particles is simulated taking into account a detailed description of the detector response to nuclearites and of the data acquisition conditions. A down-going flux of cosmic nuclearites with Galactic velocities (beta = 10(-3)) was considered for this study. The mass threshold for detecting these particles at the detector level is 4 x 10(13) GeV/c(2). Upper limits on the nuclearite flux for masses up to 10(17) GeV/c(2) at the level of similar to 5 x 10(-17) cm(-2) s(-1) sr(-1) are obtained. These are the first upper limits on nuclearites established with a neutrino telescope and the most stringent ever set for Galactic velocities.

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.

Abstract: KM3NeT is a research infrastructure being installed in the deep Mediterranean Sea. It will house a neutrino telescope comprising hundreds of networked moorings – detection units or strings – equipped with optical instrumentation to detect the Cherenkov radiation generated by charged particles from neutrino-induced collisions in its vicinity. In comparison to moorings typically used for oceanography, several key features of the KM3NeT string are different: the instrumentation is contained in transparent and thus unprotected glass spheres; two thin Dyneema (R) ropes are used as strength members; and a thin delicate backbone tube with fibre-optics and copper wires for data and power transmission, respectively, runs along the full length of the mooring. Also, compared to other neutrino telescopes such as ANTARES in the Mediterranean Sea and GVD in Lake Baikal, the KM3NeT strings are more slender to minimise the amount of material used for support of the optical sensors. Moreover, the rate of deploying a large number of strings in a period of a few years is unprecedented. For all these reasons, for the installation of the KM3NeT strings, a custom-made, fast deployment method was designed. Despite the length of several hundreds of metres, the slim design of the string allows it to be compacted into a small, re-usable spherical launching vehicle instead of deploying the mooring weight down from a surface vessel. After being lowered to the seafloor, the string unfurls to its full length with the buoyant launching vehicle rolling along the two ropes. The design of the vehicle, the loading with a string, and its underwater self-unrolling are detailed in this paper.

Abstract: The ANTARES detector is an undersea neutrino telescope in the Mediterranean Sea. The search for pointlike neutrino sources is one of the main goals of the ANTARES telescope, requiring a reliable method to evaluate the detector angular resolution and pointing accuracy. This work describes the study of the Sun “shadow” effect with the ANTARES detector. The shadow is the deficit in the atmospheric muon flux in the direction of the Sun caused by the absorption of the primary cosmic rays. This analysis is based on the data collected between 2008 and 2017 by the ANTARES telescope. The observed statistical significance of the Sun shadow detection is 3.7 sigma, with an estimated angular resolution of 0.59 degrees +/- 0.10 degrees for downward-going muons. The pointing accuracy is found to be consistent with the expectations and no evidence of systematic pointing shifts is observed.