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As gravitational-wave (GW) interferometers become more sensitive and probeever more distant reaches, the number of detected binary neutron star mergerswill increase. However, detecting more events farther away with GWs does notguarantee corresponding increase in the number of electromagnetic counterpartsof these events. Current and upcoming wide-field surveys that participate in GWfollow-up operations will have to contend with distinguishing the kilonova fromthe ever increasing number of transients they detect, many of which will beconsistent with the GW sky-localization. We have developed a novel tool basedon a temporal convolutional neural network architecture, trained on sparseearly-time photometry and contextual information for ElectromagneticCounterpart Identification (El-CID). The overarching goal for El-CID is toslice through list of new transient candidates that are consistent with the GWsky localization, and determine which sources are consistent with kilonovae,allowing limited target-of-opportunity resources to be used judiciously. Inaddition to verifying the performance of our algorithm on an extensive testingsample, we validate it on AT2017gfo - the only EM counterpart of a binaryneutron star merger discovered to date - and AT2019npv - a supernova that wasinitially suspected as a counterpart of the gravitational-wave event, GW190814,but was later ruled out after further analysis.

El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification