Data-driven core-collapse supernova multilateration with first neutrino events

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SuperNova Early Warning System (SNEWS2.0) network of neutrino and dark matter detectors aims to use the relative arrival times of the neutrinos at the different experiments to point back to the supernova so as to facilitate follow-up observation. One of the simplest methods to estimate the CCSN direction is to use the first neutrino events detected through the inverse 𝛽 decay (IBD) process, νe p → eþn. We will consider neutrino detectors sensitive to IBD interactions with low backgrounds. The difference in signal arrival times between a large and a small detector will be biased, however, with the first event at the smaller detector, on average, arriving later than that at the larger detector. This bias can be mitigated by using these first events in a data-driven approach without recourse to simulations or models. The resulting method requires, at minimum, only the times of the first events at most detectors, along with a longer time series of events from one larger detector to act as a reference lightcurve. ... mehrIn this article, we demonstrate this method and its uncertainty estimate using pairs of detectors of different sizes and with different supernova distances. Finally, we use this method to calculate probability skymaps using four detectors currently in operation, Super-Kamiokande, Jiangmen Underground Neutrino Observatory (JUNO), Large Volume Detector (LVD), and SNO+, and show that the calculated probabilities yield appropriate confidence intervals for all supernova directions. The area of the 68% confidence interval varies by distance and direction, but is expected to be a few thousand square degrees. The resulting skymaps should be useful for the multimessenger community as a rapid, initial pointing to follow up on the SNEWS2.0 Galactic CCSN neutrino alert.