On a new statistical technique for the real-time recognition of ultra-low multiplicity astrophysical neutrino burst

The real-time recognition of neutrino signals from astrophysical objects with very-low false alarm rate and short-latency, is crucial to perform multi-messenger detection, especially in the case of distant core-collapse supernovae accessible with the next generation of large-scale neutrino telescopes. The current time-based selection algorithms implemented in operating online monitors depend mainly on the number of events (multiplicity) detected in a fixed time window, under the hypothesis of Poisson-distributed background. However, these methods are not capable of exploiting the time profile discrepancies between the expected supernova neutrino burst and the stationary background. In this paper we propose a new general and flexible technique (beta filter method) which provides specific decision boundaries on the cluster multiplicity-duration plane, guaranteeing the desired false alarm rate in an analytical way. The performance is evaluated using the injection of a general purpose SN-like signal on top of realistic background rates in current detectors. An absolute gain in efficiency of up to $\sim 80\%$ is achieved compared with the standard techniques, and a new ultra-low multiplicity region is unveiled.