TL;DR
El-CID is a neural network-based tool designed to efficiently identify kilonova electromagnetic counterparts of gravitational-wave events from transient surveys, optimizing follow-up observations.
Contribution
We introduce El-CID, a novel temporal convolutional neural network that improves electromagnetic counterpart identification for gravitational-wave events using early-time photometry.
Findings
El-CID achieves high accuracy in identifying kilonovae.
Validated on AT2017gfo, successfully distinguishing it as a GW counterpart.
Demonstrated effectiveness in differentiating kilonovae from other transients.
Abstract
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…
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