The Journal of Astronomical Telescopes, Instruments, and Systems (JATIS) publishes peer-reviewed papers reporting on original research in the development, testing, and application of telescopes, instrumentation, techniques, and systems for ground- and space-based astronomy.

KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained.

<!-- *** Custom HTML *** --> 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.

Hydrophones used in Passive Acoustic Monitoring generate vast amounts of data, with the storage requirements for raw signals dependent on the sampling frequency, which limits the range of frequencies that can be recorded. Since the installation of these observatories is costly, it is crucial to maximize the utility of high-sampling-rate recordings to expand the range of survey types. However, storing these large datasets for long-term trend analysis presents significant challenges. This paper proposes an approach that reduces the data storage requirements by up to 85% while preserving critical information about Power Spectral Density and Sound Pressure Level. The strategy involves generating these key metrics from spectrograms, enabling both short-term (micro) and long-term (macro) studies. A proposal for efficient data processing is presented, structured in three steps: the first focuses on generating key metrics to replace space-consuming raw signals, the second addresses the treatment of these metrics for long-term studies, and the third outlines the creation of event detectors from the processed metrics. A comprehensive overview of the essential features for analyzing acoustic signals is provided, along with considerations for the future design of marine observatories. The necessary calculations and processes are detailed, demonstrating the potential of these methods to address the current data storage and processing limitations in long-term acoustic monitoring.