  As gravitational-wave (GW) interferometers become more sensitive and probe
ever more distant reaches, the number of detected binary neutron star mergers
will increase. However, detecting more events farther away with GWs does not
guarantee corresponding increase in the number of electromagnetic counterparts
of these events. Current and upcoming wide-field surveys that participate in GW
follow-up operations will have to contend with distinguishing the kilonova from
the ever increasing number of transients they detect, many of which will be
consistent with the GW sky-localization. We have developed a novel tool based
on a temporal convolutional neural network architecture, trained on sparse
early-time photometry and contextual information for Electromagnetic
Counterpart Identification (El-CID). The overarching goal for El-CID is to
slice through list of new transient candidates that are consistent with the GW
sky localization, and determine which sources are consistent with kilonovae,
allowing limited target-of-opportunity resources to be used judiciously. In
addition to verifying the performance of our algorithm on an extensive testing
sample, we validate it on AT2017gfo - the only EM counterpart of a binary
neutron star merger discovered to date - and AT2019npv - a supernova that was
initially suspected as a counterpart of the gravitational-wave event, GW190814,
but was later ruled out after further analysis.
