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

TL;DR

This paper introduces RTS$^2$, a real-time statistical method for detecting weak neutrino bursts from supernovae by exploiting temporal and spatial structures, improving detection efficiency while controlling false alarms.

Contribution

The paper presents RTS$^2$, a novel real-time test statistic that enhances weak signal detection in neutrino observatories by incorporating temporal and spatial information.

Findings

RTS$^2$ improves detection efficiency for low-energy neutrino bursts.

The method maintains a constant false alarm rate for Poisson background.

RTS$^2$ is adaptable for real-time monitoring and alert systems.

Abstract

This paper proposes a new approach for the selection of low-energy neutrino bursts, such as the ones detected after a supernova. It exploits the temporal structure of the expected signal with respect to the more diffuse background by defining a "Real-time Test Statistic" (RTS) that would allow identifying very weak signals, hard to select using standard clustering methods. For a given background rate, the new method (RTS$^2$: RTS for Supernovae) increases signal efficiency while keeping the same false alarm rate for Poisson-distributed background. By adding a spatial penalty term to the definition of RTS, one can also reject spatially-correlated backgrounds such as the ones due to spallation events. Furthermore, the method is easy to implement in a real-time monitoring system as RTS can be computed recursively for successive events, and it can be easily adapted for detectors of all scales that may want to send prompt alerts e.g. through SNEWS 2.0 network.