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

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

Comparison of the measured atmospheric muon rate with Monte Carlo simulations and sensitivity study for detection of prompt atmospheric muons with KM3NeT

The KM3NeT Collaboration has successfully deployed the first detection units of the next genera- tion undersea neutrino telescopes in the Mediterranean Sea at the two sites in Italy and in France. The data sample collected between December 2016 and January 2020 has been used to measure the atmospheric muon rate at two different depths under the sea level: 3.5 km with KM3NeT- ARCA and 2.5 km with KM3NeT-ORCA. Atmospheric muons represent an abundant signal in a neutrino telescope and can be used to test the reliability of the Monte Carlo simulation chain and to study the physics of extensive air showers caused by highly-energetic primary nuclei impinging the Earth’s atmosphere. At energies above PeV the contribution from prompt muons, created right after the first interaction in the shower, is expected to become dominant, however its existence has not yet been experimentally confirmed. In this talk, data collected with the first detection units of KM3NeT are compared to Monte Carlo simulations based on MUPAGE and CORSIKA codes. The main features of the simulation and reconstruction chains are presented. Additionally, the first results of the simulated signal from the prompt muon component for KM3NeT-ARCA and KM3NeT-ORCA obtained with CORSIKA are discussed

First neutrino oscillation measurement in KM3NeT/ORCA

The KM3NeT/ORCA is a next-generation neutrino detector currently under construction in the Mediterranean Sea. There are currently 6 Detection Units deployed, and in the past year the detector has been steadily taking data. Here the first neutrino oscillation measurement is presented using data taken with the ORCA detector 6 Detection Units, containing 354.6 days of exposure. Selection criteria are discussed, followed by a neutrino oscillation analysis. In the analysis it is found that oscillations are preferred with a confidence level of 5.9 σ over "no oscillations". Likelihood scans of the Δm231 and sin2θ23 parameter also show a strong exclusion of the no oscillation hypothesis. The sensitivity contour in (sin2θ23,Δm231) is presented, showing results that are approaching to being being competitive with other experiments

KM3NeT Detection Unit Line Fit reconstruction using positioning sensors data

The KM3NeT collaboration is constructing two large neutrino detectors in the Mediterranean Sea: KM3NeT/ARCA, located near Sicily and aiming at neutrino astronomy, and KM3NeT/ORCA, located near Toulon and designed for neutrino oscillation studies. The two detectors, together, will have hundreds of Detection Units (DUs) with 18 Digital Optical Modules (DOMs) maintained vertical by buoyancy, forming a large 3D optical array for detecting the Cherenkov light produced by particle produced in neutrino interactions. To properly reconstruct the direction of the incoming neutrino, the position of the DOMs must be known precisely with an accuracy of less than 10 cm, and since the DUs are affected by sea current the position will be measured every 10 minutes. For this purpose, there are acoustic and orientation sensors inside the DOMs. An Attitude Heading Reference System (AHRS) chip provides the components values of the Acceleration and Magnetic field in the DOM, from which it is possible to calculate Yaw, Pitch, and Roll for each floor of the line. A piezo sensor detects the signals from fixed acoustic emitters on the sea floor, so to position it by trilateration. Data from these sensors are used as an input to reconstruct the shape of the entire line based on a DU Line Fit mechanical model. This poster presents an overview of the KM3NeT monitoring system, as well as the line fit model and its results

Indirect dark matter searches with neutrinos from the Galactic Centre region with the ANTARES and KM3NeT telescopes

An anomalous flux of neutrinos produced in hypothetical annihilations or decays of dark matter inside a source would produce a signal observable with neutrino telescopes. As suggested by observations, a conspicuous amount of dark matter is believed to accumulate in the centre of our Galaxy, which is in neat visibility for the Mediterranean underwater telescopes ANTARES and KM3NeT. Searches have been conducted with a maximum likelihood method to identify the presence of a dark matter signature in the neutrino flux measured by ANTARES. Results of all-flavour searches for WIMPs with masses from 50 GeV/c2 up to 100 TeV/c2 over the whole operation period from 2007 to 2020 are presented here. Alternative scenarios which propose a dark matter candidate in the heavy sector extensions of the Standard Model would produce a clear signature in the ANTARES telescope, that can exploit its view of the Galactic Centre up to high energies. The presentation of Galactic Centre searches is completed with ongoing analyses and future potential of the KM3NeT telescope, in phased construction in the Mediterranean Sea

Search for nuclearites with the KM3NeT detector

Strange quark matter (SQM) is a hypothetical type of matter composed of almost equal quantities of up, down and strange quarks. In [1], Edward Witten presented the SQM as a denser and more stable matter that could represent the ground state of Quantum Chromodynamics (QCD). Massive SQM particles are called nuclearites. These particles could have been produced in violent astrophysical processes, such as neutron star collisions and could be present in the cosmic radiation. Nuclearites with masses greater than 1013 GeV and velocities of about 250 km/s (typical galactic velocities) could reach the Earth and interact with atoms and molecules of sea water within the sensitive volume of the deep-sea neutrino telescopes. The SQM particles can be detected with the KM3NeT telescope (whose first lines are already installed and taking data in the Mediterranean Sea) through the visible blackbody radiation generated along their path inside or near the instrumented area. In this work the results of a study using Monte Carlo simulations of down-going nuclearites are discussed