SNAX: Supernova Neutrino Analysis in XENONnT

Core-collapse supernovae emit 99% of their energy as neutrinos, preceding any optical signals, offering a unique opportunity to study the physics of these explosive events. While traditional neutrino detectors are optimized for specific flavors, dark matter experiments like XENONnT leverage coherent elastic neutrino-nucleus scattering (CEνNS), enabling detection of neutrinos across all flavors at low energies.

This presentation focuses on the methods developed within the XENONnT framework to identify supernova neutrino signals promptly. We discuss the simulation of neutrino interactions, strategies for detecting CEνNS signals in real-time, and the integration of an active software trigger to communicate with the Supernova Early Warning System (SNEWS). These techniques ensure efficient and timely detection, allowing dark matter detectors to complement traditional neutrino observatories.

This work is supported in part through the Helmholtz Initiative and Networking Fund (grant no. W2/W3-118). Support by the graduate school KSETA at KIT is gratefully acknowledged.