Event reconstruction for KM3NeT/ORCA using convolutional neural networks

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

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector 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

KM3NeT tower data acquisition and data transport electronics

In the framework of the KM3Net European project, the production stage of a large volume underwater neutrino telescope has started. The forthcoming installation includes 8 towers and 24 strings, that will be installed 100 km off-shore Capo Passero (Italy) at 3500 m depth. The KM3NeT tower, whose layout is strongly based on the NEMO Phase-2 prototype tower deployed in March 2013, has been re-engineered and partially re-designed in order to optimize production costs, power consumption, and usability. This contribution gives a description of the main electronics, including front-end, data transport and clock distribution system, of the KM3NeT tower detection unit

Deep seawater inherent optical properties in the Southern Ionian Sea

The NEMO (NEutrino Mediterranean Observatory) Collaboration has been carrying out since 1998 an evaluation programme of deep sea sites suitable for the construction of the future Mediterranean km(3) Cerenkov neutrino telescope. We investigated the seawater optical and oceanographic properties of several deep sea marine areas close to the Italian Coast. Inherent optical properties (light absorption and attenuation coefficients) have been measured as a function of depth using an experimental apparatus equipped with standard oceanographic probes and the commercial transmissometer AC9 manufactured by WETLabs. This paper reports on the visible light absorption and attenuation coefficients measured in deep seawater of a marine region located in the Southern Ionian Sea, 60-100 km SE of Cape, Passero (Sicily). Data show that blue light absorption coefficient is about 0.015 m(-1) (corresponding to an absorption length of 67 m) close to the one of optically pure water and it does not show seasonal variation

The optical modules of the phase-2 of the NEMO project

A 13-inch Optical Module (OM) containing a large-area (10-inch) photomultiplier was designed as part of Phase-2 of the NEMO project. An intense R&D activity on the photomul- tipliers, the voltage supply boards, the optical coupling as well as the study of the influences of the Earth’s magnetic field has driven the choice of each single component of the OM. Following a well-established production procedure, 32 OMs were assembled and their functionality tested. The design, the testing and the production phases are thoroughly described in this paper

A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial

Multi-element interventions for first-episode psychosis (FEP) are promising, but have mostly been conducted in non-epidemiologically representative samples, thereby raising the risk of underestimating the complexities involved in treating FEP in 'real-world' services

KM3NeT front-end and readout electronics system: hardware, firmware, and software

he KM3NeT research infrastructure being built at the bottom of the Mediterranean Sea will host water-Cherenkov telescopes for the detection of cosmic neutrinos. The neutrino telescopes will consist of large volume three-dimensional grids of optical modules to detect the Cherenkov light from charged particles produced by neutrino-induced interactions. Each optical module houses 31 3-in. photomultiplier tubes, instrumentation for calibration of the photomultiplier signal and positioning of the optical module, and all associated electronics boards. By design, the total electrical power consumption of an optical module has been capped at seven Watts. We present an overview of the front-end and readout electronics system inside the optical module, which has been designed for a 1-ns synchronization between the clocks of all optical modules in the grid during a life time of at least 20 years