Using machine learning for transient classification in searches for gravitational-wave counterparts

Understand

The large sky localization regions offered by the gravitational-wave interferometers require efficient follow-up of the many counterpart candidates identified by the wide field-of-view telescopes.

• Given the restricted telescope time, the creation of prioritized lists of the many identified candidates becomes mandatory.
• Towards this end, we use \text{\astrorapid}, a multi-band photometric lightcurve classifier, to differentiate between kilonovae, supernovae, and other possible transients.
• We demonstrate our method on the photometric observations of real events.