Observational analysis detects diffuse astrophysical neutrinos using the KM3NeT/ARCA telescope, indicating machine learning enhances event selection.
The KM3NeT research infrastructure comprises two submarine Cherenkov neutrino telescopes at two corresponding abyssal sites in the Mediterranean Sea. KM3NeT/ARCA is being built off Portopalo di Capo Passero, Sicily, Italy, at a depth of 3500 m. While collecting data already in a partial configuration, KM3NeT/ARCA aims at a volume of 1 km³ instrumented with a three-dimensional array of thousands of optical modules, whose geometric arrangement is optimised for the measurement of high energy neutrinos (> TeV) from astrophysical sources. In this contribution, the expectations of KM3NeT/ARCA for the detection of the diffuse flux of astrophysical neutrinos are presented. The analysis is performed using Monte Carlo simulated events assuming a complete detector configuration. Machine learning techniques are used for event selection, while for statistical analysis both a Bayesian and a frequentist method are employed.
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Karagiannis et al. (2025) studied this question.
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