Title: Simulation chain and signal classification for acoustic neutrino detection in seawater,
Abstract: Acoustic neutrino detection is a promising approach to extend the energy
range of neutrino telescopes to energies beyond $10^{18}$\,eV. Currently
operational and planned water-Cherenkov neutrino telescopes, most notably
KM3NeT, include acoustic sensors in addition to the optical ones. These
acoustic sensors could be used as instruments for acoustic detection, while
their main purpose is the position calibration of the detection units. In this
article, a Monte Carlo simulation chain for acoustic detectors will be
presented, covering the initial interaction of the neutrino up to the signal
classification of recorded events. The ambient and transient background in the
simulation was implemented according to data recorded by the acoustic set-up
AMADEUS inside the ANTARES detector. The effects of refraction on the neutrino
signature in the detector are studied, and a classification of the recorded
events is implemented. As bipolar waveforms similar to those of the expected
neutrino signals are also emitted from other sound sources, additional features
like the geometrical shape of the propagation have to be considered for the
signal classification. This leads to a large improvement of the background
suppression by almost two orders of magnitude, since a flat cylindrical
"pancake" propagation pattern is a distinctive feature of neutrino signals. An
overview of the simulation chain and the signal classification will be
presented and preliminary studies of the performance of the classification will
be discussed.