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

TL;DR

This paper presents a machine learning approach using ext{ extastrorapid} to classify transient astronomical events from photometric lightcurves, aiding rapid identification of gravitational-wave counterparts.

Contribution

It introduces a multi-band photometric classifier for transient differentiation and demonstrates its effectiveness on real and simulated data for quick candidate prioritization.

Findings

After a few days of observation, supernovae can be ruled out as counterparts.

The classifier performs well on both real and simulated lightcurves.

Rapid classification helps optimize telescope follow-up efforts.

Abstract

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. In addtion, the classification performance is tested on simulated lightcurves, both ideally and realistically sampled. We show that after only a few days of observations of an astronomical object, it is possible to rule out candidates as supernovae and other known transients.