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The large sky localization regions offered by the gravitational-waveinterferometers require efficient follow-up of the many counterpart candidatesidentified by the wide field-of-view telescopes. Given the restricted telescopetime, the creation of prioritized lists of the many identified candidatesbecomes mandatory. Towards this end, we use \text{\astrorapid}, a multi-bandphotometric lightcurve classifier, to differentiate between kilonovae,supernovae, and other possible transients. We demonstrate our method on thephotometric observations of real events. In addtion, the classificationperformance is tested on simulated lightcurves, both ideally and realisticallysampled. We show that after only a few days of observations of an astronomicalobject, it is possible to rule out candidates as supernovae and other knowntransients.

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