Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF

Abstract The follow-up of gravitational-wave events by wide-field surveys is a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using simulated low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). It uses a bidirectional long-short-term memory model to forecast kilonova light curves from binary neutron star and neutron star–black hole mergers in the Zwicky Transient Facility (ZTF) and Rubin Observatory’s Legacy Survey of Space and Time filters. The model achieves a test mean squared error (MSE) of 0.12 for ZTF filters and 0.23 for Rubin filters, calculated by averaging the squared error over all time steps, filters, and light curves in the test set. We evaluate the performance of the model against merger events followed-up by the ZTF partnership during O4c. We also analyze the effect of incorporating constraints on physical features such as ejecta mass. Using ejecta mass, the performance of the model improves to an MSE of 0.1 for ZTF filters and 0.15 for Rubin filters. Our model is publicly available and can help to add important information to help plan follow-up of candidate events discovered by current and next-generation public surveys.