Abstract: The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes. Program summary Program title: Embedded software for the KM3NeT CLB CPC Library link to program files: https://doi.org/10.17632/s847hpsns4.1 Licensing provisions: GNU General Public License 3 Programming language: C Nature of problem: The challenge for the embedded software in the KM3NeT neutrino telescope lies in orchestrating the Digital Optical Modules (DOMs) to achieve the synchronized data acquisition of the incoming optical signals. The DOMs are the crucial component responsible for capturing neutrino interactions deep underwater. The embedded software must configure and precisely time the operation of each DOM. Any deviation or timing mismatch could compromise data integrity, undermining the scientific value of the experiment. Therefore, the embedded software plays a critical role in coordinating, synchronizing, and operating these modules, ensuring they work in unison to capture and process neutrino signals accurately, ultimately advancing our understanding of fundamental particles in the Universe. Solution method: The embedded software on the DOMs provides a solution based on a C-based bare-metal application, operating without a real-time embedded OS. It is loaded into the RAM during FPGA configuration, consuming less than 256 kB of RAM. The software architecture comprises two layers: system software and application. The former offers OS-like features, including a multitasking scheduler, firmware updates, peripheral drivers, a UDP-based network stack, and error handling utilities. The application layer contains a state machine ensuring consistent program states. It is navigated via slow control events, including external inputs and autonomous responses. Subsystems within the application code control specific acquisition electronics components via the associated driver abstractions. Additional comments including restrictions and unusual features: Due to the operation conditions of the neutrino telescope, where access is restricted, the embedded software implements a fail-safe procedure to reconfigure the firmware where the embedded software runs.

Abstract: As part of the European LAGUNA design study on a next-generation neutrino detector, we propose the liquid-scintillator detector LENA (Low Energy Neutrino Astronomy) as a multipurpose neutrino observatory. The outstanding successes of the Borexino and KamLAND experiments demonstrate the large potential of liquid-scintillator detectors in low-energy neutrino physics. Low energy threshold, good energy resolution and efficient background discrimination are inherent to the liquid-scintillator technique. A target mass of 50 kt will offer a substantial increase in detection sensitivity. At low energies, the variety of detection channels available in liquid scintillator will allow for an energy and flavor-resolved analysis of the neutrino burst emitted by a galactic Supernova. Due to target mass and background conditions, LENA will also be sensitive to the faint signal of the Diffuse Supernova Neutrino Background. Solar metallicity, time-variation in the solar neutrino flux and deviations from MSW-LMA survival probabilities can be investigated based on unprecedented statistics. Low background conditions allow to search for dark matter by observing rare annihilation neutrinos. The large number of events expected for geoneutrinos will give valuable information on the abundances of Uranium and Thorium and their relative ratio in the Earth's crust and mantle. Reactor neutrinos enable a high-precision measurement of solar mixing parameters. A strong radioactive or pion decay-at-rest neutrino source can be placed close to the detector to investigate neutrino oscillations for short distances and sub-MeV to MeV energies. At high energies, LENA will provide a new lifetime limit for the SUSY-favored proton decay mode into kaon and antineutrino, surpassing current experimental limits by about one order of magnitude. Recent studies have demonstrated that a reconstruction of momentum and energy of GeV particles is well feasible in liquid scintillator. Monte Carlo studies on the reconstruction of the complex event topologies found for neutrino interactions at multi-GeV energies have shown promising results. If this is confirmed. LENA might serve as far detector in a long-baseline neutrino oscillation experiment currently investigated in LAGUNA-LBNO.

Abstract: The T2K experiment widely uses plastic scintillator as a target for neutrino interactions and an active medium for the measurement of charged particles produced in neutrino interactions at its near detector complex. Over 10 years of operation the measured light yield recorded by the scintillator based subsystems has been observed to degrade by 0.9-2.2% per year. Extrapolation of the degradation rate through to 2040 indicates the recorded light yield should remain above the lower threshold used by the current reconstruction algorithms for all subsystems. This will allow the near detectors to continue contributing to important physics measurements during the T2K-II and Hyper-Kamiokande eras. Additionally, work to disentangle the degradation of the plastic scintillator and wavelength shifting fibres shows that the reduction in light yield can be attributed to the ageing of the plastic scintillator. The long component of the attenuation length of the wavelength shifting fibres was observed to degrade by 1.3-5.4% per year, while the short component of the attenuation length did not show any conclusive degradation.

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 Hamamatsu R12199-023-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum efficiency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.