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The real-time recognition of neutrino signals from astrophysical objects withvery-low false alarm rate and short-latency, is crucial to performmulti-messenger detection, especially in the case of distant core-collapsesupernovae accessible with the next generation of large-scale neutrinotelescopes. The current time-based selection algorithms implemented inoperating online monitors depend mainly on the number of events (multiplicity)detected in a fixed time window, under the hypothesis of Poisson-distributedbackground. However, these methods are not capable of exploiting the timeprofile discrepancies between the expected supernova neutrino burst and thestationary background.  In this paper we propose a new general and flexible technique (beta filtermethod) which provides specific decision boundaries on the clustermultiplicity-duration plane, guaranteeing the desired false alarm rate in ananalytical way. The performance is evaluated using the injection of a generalpurpose SN-like signal on top of realistic background rates in currentdetectors. An absolute gain in efficiency of up to $\sim 80\%$ is achievedcompared with the standard techniques, and a new ultra-low multiplicity regionis unveiled.

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