ADASSX

Surveys are 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 low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). This method uses a bidirectional long-short-term memory (LSTM) 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 0.19 on average across all ZTF and Rubin filters. We verify the performance of the model against merger events followed-up by the ZTF partnership during O4a and O4b. We also analyze the effect of incorporating skymaps and constraints on physical features such as ejecta mass through a hybrid convolutional neural network and LSTM. Using mass ejecta, the performance of the model improves to an MSE of 0.1. However, using full skymap information results in slightly lower model performance. Our models are 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.