Abstract: 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 similar to 16 GeV to 100 GeV, have been analysed. A log-likelihood ratio test of the dimensionless coefficients epsilon(mu tau) and epsilon(tau tau) – epsilon(mu mu) does not provide clear evidence of deviations from standard interactions. For normal neutrino mass ordering, the combined fit of both coefficients yields a value 1.7 sigma away from the null result. However, the 68% and 95% confidence level intervals for epsilon(mu tau) and epsilon(tau tau) – epsilon(mu mu), 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 epsilon(mu tau) is among the most stringent to date and it further restrains the strength of possible non-standard interactions in the μ- tau sector.

Abstract: A workshop was held at Fermilab November 8-9, 2013 to discuss the challenges of using high voltage in noble liquids. The participants spanned the fields of neutrino, dark matter, and electric dipole moment physics. All presentations at the workshop were made in plenary sessions. This document summarizes the experiences and lessons learned from experiments in these fields at developing high voltage systems in noble liquids.

Abstract: During the commissioning of the first of the two detectors of the Double Chooz experiment, an unexpected and dominant background caused by the emission of light inside the optical volume has been observed. A specific study of the ensemble of phenomena called Light Noise has been carried out in-situ, and in an external laboratory, in order to characterize the signals and to identify the possible processes underlying the effect. Some mechanisms of instrumental noise originating from the PMTs were identified and it has been found that the leading one arises from the light emission localized on the photomultiplier base and produced by the combined effect of heat and high voltage across the transparent epoxy resin covering the electric components. The correlation of the rate and the amplitude of the signal with the temperature has been observed. For the first detector in operation the induced background has been mitigated using online and offline analysis selections based on timing and light pattern of the signals, while a modification of the photomultiplier assembly has been implemented for the second detector in order to blacken the PMT bases.

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.