Core-collapse supernovae (CCSNe) represent significant astronomical occurrences that offer essential insights into galaxy dynamics. The temporal pattern of neutrinos during these events serves as a distinctive and valuable information source, shedding light on collapsing star mechanisms and particle behaviors in densely packed environments. Despite the rarity of nearby supernovae, one observation of supernova neutrinos has been recorded to date. To optimize our understanding during the next galactic CCSN, it is crucial to amalgamate real-time observations from multiple neutrino experiments and promptly convey the results to optical telescopes. However, pinpointing the CCSN poses a substantial challenge, requiring the separation of localization information from signatures associated with supernova progenitor properties or neutrino physics. Existing CCSN distance measurement algorithms assume accurate predictions of neutrino properties by the Standard Model. This contribution introduces an approach to rapidly and effectively extract and distinguish information about CCSN and neutrino physics. We demonstrate the robustness of this approach against potential biases in CCSN measurements due to new physics effects, leveraging the diverse capabilities of next-generation neutrino detectors.