Application of Kalman filter methods to event filtering and reconstruction for neutrino telescopy

Event reconstruction in underwater neutrino telescopes suffers from a high background noise due to the 40K decays. Adaptive algorithms are able to suppress automatically such a noise and therefore are considered as good candidates for track fitting at the KM3NeT environment. In this note we describe an iterative event filtering and track reconstruction technique, employing Kalman filter methods and we present results from a detailed simulation study concerning the KM3NeT detector. We evaluate the accuracy of this technique and we compare its efficiency with other standard track reconstruction methods.