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This paper proposes a new approach for the selection of low-energy neutrinobursts, such as the ones detected after a supernova. It exploits the temporalstructure of the expected signal with respect to the more diffuse background bydefining a "Real-time Test Statistic" (RTS) that would allow identifying veryweak signals, hard to select using standard clustering methods. For a givenbackground rate, the new method (RTS$^2$: RTS for Supernovae) increases signalefficiency while keeping the same false alarm rate for Poisson-distributedbackground. By adding a spatial penalty term to the definition of RTS, one canalso reject spatially-correlated backgrounds such as the ones due to spallationevents. Furthermore, the method is easy to implement in a real-time monitoringsystem as RTS can be computed recursively for successive events, and it can beeasily adapted for detectors of all scales that may want to send prompt alertse.g. through SNEWS 2.0 network.

Identification of neutrino bursts associated to supernovae with Real-time Test Statistic (RTS$^2$) method