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

Cosmin Stachie, Michael W. Coughlin, Nelson Christensen, Daniel Muthukrishna

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 astrorapid, a multiband photometric light-curve classifier, to differentiate between kilonovae, supernovae, and other possible transients. We demonstrate our method on the photometric observations of real events. In addition, the classification performance is tested on simulated light curves, 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.

Original languageEnglish (US)
Pages (from-to)1320-1331
Number of pages12
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
StatePublished - Sep 1 2020

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  • Gravitational waves


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