Abstract: KM3NeT/ORCA is a next-generation neutrino telescope optimised for atmospheric neutrino oscillations studies. In this paper, the sensitivity of ORCA to the presence of a light sterile neutrino in a 3+1 model is presented. After three years of data taking, ORCA will be able to probe the active-sterile mixing angles theta(14), theta(24), theta(34) and the effective angle theta(mu e), over a broad range of mass squared difference Delta m(41)(2) similar to [10(-5), 10] eV(2), allowing to test the eV-mass sterile neutrino hypothesis as the origin of short baseline anomalies, as well as probing the hypothesis of a very light sterile neutrino, not yet constrained by cosmology. ORCA will be able to explore a relevant fraction of the parameter space not yet reached by present measurements.

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: 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.