Speaker
Description
Ultra-high-energy neutrinos are a gateway into new physics beyond the Stan-
dard Model, with potentials for unraveling some of the essential mysteries in
cosmology and astrophysics. Given the nature of neutrinos, with electric charge
neutrality and negligible mass, they go through cosmic distances without in-
teracting and will be an excellent candidate for multi-messenger astronomy
in studying celestial objects, especially black holes. It is actually these very
attributes that constitute the difficulties in the attempts at the detection of
neutrinos.
The KM3NeT experiment tackles this challenge by deploying one cubic kilo-
meter of instrumented volume at the bottom of the Mediterranean Sea. This
contribution describes an innovative study related to the detection of ultra-high-
energy neutrinos by means of their acoustic signal; it not only gives additional
information but is also cost-efficient to implement in the frame of the existing
infrastructure called KM3NeT and opens up perspectives for an improvement
of the detection accuracy.
We also recommend advanced computational techniques, namely deep learn-
ing algorithms, for neutrino event reconstruction. These state-of-the-art data
analysis tools will serve to increase the accuracy and reduce the errors associated
with traditional methods, thus helping to advance neutrino astronomy.
