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The simultaneous observation of gravitational waves (GW) and promptelectromagnetic counterparts from the merger of two neutron stars can helpreveal the properties of extreme matter and gravity during and immediatelyafter the final plunge. Rapid sky localization of these sources is crucial tofacilitate such multi-messenger observations. Since GWs from binary neutronstar (BNS) mergers can spend up to 10-15 mins in the frequency bands of thedetectors at design sensitivity, early warning alerts and pre-merger skylocalization can be achieved for sufficiently bright sources, as demonstratedin recent studies. In this work, we present pre-merger BNS sky localizationresults using CBC-SkyNet, a deep learning model capable of inferring skylocation posterior distributions of GW sources at orders of magnitude fasterspeeds than standard Markov Chain Monte Carlo methods. We test our model'sperformance on a catalog of simulated injections from Sachdev et al. (2020),recovered at 0-60 secs before merger, and obtain comparable sky localizationareas to the rapid localization tool BAYESTAR. These results show thefeasibility of our model for rapid pre-merger sky localization and thepossibility of follow-up observations for precursor emissions from BNS mergers.

Pre-merger sky localization of gravitational waves from binary neutron star mergers using deep learning