Core-collapse supernovae (CCSNe) are important astronomical events that provide crucial information about the dynamics of the Universe. Time profile of neutrinos in core-collapse supernovae is a unique and valuable source of information about the mechanism of collapsing stars and the behavior of particles in highly dense environments. However, as close-by supernovae are rare, only one observation of supernova neutrinos has been made to date. To make the most of the next galactic CCSN, observations from multiple experiments need to be combined in real-time, and the results quickly transmitted to telescopes. Locating the CCSN will notably be a major challenge and requires disentangling localization information from signatures associated with the supernova progenitor properties or the physics of the neutrinos themselves. Consequently, existing CCSN distance measurement algorithms need to assume that neutrino properties are well-predicted by the Standard Model. In this contribution we present an approach to extract and separate information about the CCSN and the physics of the neutrinos in a fast and simple way. We show how this approach can be made robust against the new physics effects most susceptible to bias CCSN measurements, by taking advantage of the diverse landscape of next-generation neutrino detectors.