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 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: 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.

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 ATLAS experiment is constructing new all-silicon inner tracking system for HL-LHC. The strip detectors cover the radial extent of 40 to 100 cm. A new approach is adopted to use p-type silicon material, making the readout in n+-strips, so-called n+-in-p sensors. This allows for enhanced radiation tolerance against an order of magnitude higher particle fluence compared to the LHC. To cope with varying hit rates and occupancies as a function of radial distance, there are two barrel sensor types, the short strips (SS) for the inner 2 and the long strips (LS) for the outer 2 barrel cylinders, respectively. The barrel sensors exhibit a square, 9.8 x 9.8 cm2, geometry, the largest possible sensor area from a 6-inch wafer. The strips are laid out in parallel with a strip pitch of 75.5 μm and 4 or 2 rows of strip segments. The strips are AC-coupled and biased via polysilicon resistors. The endcap sensors employ a “stereo-annulus” geometry exhibiting a skewed-trapezoid shapes with circular edges. They are designed in 6 unique shapes, R0 to R5, corresponding to progressively increasing radial extents and which allows them to fit within the petal geometry and the 6-inch wafer maximally. The strips are in fan-out geometry with an in-built rotation angle, with a mean pitch of approximately 75 μm and 4 or 2 rows of strip segments. The eight sensor types are labeled as ATLAS18xx where xx stands for SS, LS, and R0 to R5. According to the mechanical and electrical specifications, CAD files for wafer processing were laid out, following the successful designs of prototype barrel and endcap sensors, together with a number of optimizations. A pre-production was carried out prior to the full production of the wafers. The quality of the sensors is reviewed and judged excellent through the test results carried out by vendor. These sensors are used for establishing acceptance procedures and to evaluate their performance in the ATLAS collaboration, and subsequently for pre-production of strip modules and stave and petal structures.