Speaker
Description
Neutrinos from core-collapse supernovae provide a unique window into neutrino properties and flavor evolution under extreme conditions, offering sensitivity to neutrino oscillations, the Mikheyev–Smirnov–Wolfenstein (MSW) effect, and the neutrino mass ordering, while enabling real-time multimessenger observations. The DarkSide-20k experiment is capable of probing these phenomena as a dual-phase liquid argon time projection chamber sensitive to supernova neutrinos through both coherent elastic neutrino–nucleus scattering (CE$\nu$NS) and the charged-current interaction $^{40}$Ar($\nu_e$,e)$^{40}$K.
Real-time identification of supernova neutrino signals in DarkSide-20k requires a robust online trigger operating under stringent constraints, including limited time for data processing and strict false-positive requirements. To meet these challenges, trigger performance and detection strategies are developed and validated using full Geant4-based simulations. We present prospects for online supernova neutrino detection in DarkSide-20k, comparing traditional statistical burst-search techniques with neural network–based methods optimized for real-time operation, and assess sensitivity, background rejection, and false alarm control under realistic conditions. These studies illustrate the potential of DarkSide-20k to operate as a competitive real-time supernova neutrino observatory and to contribute to global alert systems such as the SuperNova Early Warning System (SNEWS2.0).