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.

Abstract: Collapsing supermassive stars (M greater than or similar to 3 x 10(4) M-circle dot) at high redshifts can naturally provide seeds and explain the origin of the supermassive black holes observed in the centers of nearly all galaxies. During the collapse of supermassive stars, a burst of non-thermal neutrinos is generated with a luminosity that could greatly exceed that of a conventional core collapse supernova explosion. In this work, we investigate the extent to which the neutrinos produced in these explosions can be observed via coherent elastic neutrino-nucleus scattering (CEvNS). Large scale direct dark matter detection experiments provide particularly favorable targets. We find that upcoming O(100) tonne-scale experiments will be sensitive to the collapse of individual supermassive stars at distances as large as O(10) Mpc.

Abstract: Liquid scintillators are a common choice for neutrino physics experiments, but their capabilities to perform background rejection by scintillation pulse shape discrimination is generally limited in large detectors. This paper describes a novel approach for a pulse shape based event classification developed in the context of the Double Chooz reactor antineutrino experiment. Unlike previous implementations, this method uses the Fourier power spectra of the scintillation pulse shapes to obtain event-wise information. A classification variable built from spectral information was able to achieve an unprecedented performance, despite the lack of optimization at the detector design level. Several examples of event classification are provided, ranging from differentiation between the detector volumes and an efficient rejection of instrumental light noise, to some sensitivity to the particle type, such as stopping muons, ortho-positronium formation, alpha particles as well as electrons and positrons. In combination with other techniques the method is expected to allow for a versatile and more efficient background rejection in the future, especially if detector optimization is taken into account at the design level.

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: The T2K experiment widely uses plastic scintillator as a target for neutrino interactions and an active medium for the measurement of charged particles produced in neutrino interactions at its near detector complex. Over 10 years of operation the measured light yield recorded by the scintillator based subsystems has been observed to degrade by 0.9-2.2% per year. Extrapolation of the degradation rate through to 2040 indicates the recorded light yield should remain above the lower threshold used by the current reconstruction algorithms for all subsystems. This will allow the near detectors to continue contributing to important physics measurements during the T2K-II and Hyper-Kamiokande eras. Additionally, work to disentangle the degradation of the plastic scintillator and wavelength shifting fibres shows that the reduction in light yield can be attributed to the ageing of the plastic scintillator. The long component of the attenuation length of the wavelength shifting fibres was observed to degrade by 1.3-5.4% per year, while the short component of the attenuation length did not show any conclusive degradation.