Speaker
Description
The COHERENT experiment has demonstrated coherent elastic neutrino–nucleus scattering (CEvNS) at the Spallation Neutron Source, establishing a powerful neutral-current channel for probing all neutrino flavors. Tonne-scale CEvNS detectors, especially the cryogenic CsI detector, offer a promising opportunity to detect neutrinos from a Galactic core-collapse supernova. A key challenge is that the expected supernova neutrino signal is overwhelmed by backgrounds by several orders of magnitude. In this talk, I will present a self-trigger strategy for a cryogenic CsI detector that combines simple waveform-level selections with machine-learning-based event classification to suppress backgrounds and enhance sensitivity to supernova neutrino signal. I will also discuss potential options for integrating this self-trigger framework into the detector DAQ system.