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

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 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
Volume497
Issue number2
DOIs
StatePublished - Sep 1 2020

Bibliographical note

Funding Information:
MC is supported by the David and Ellen Lee Prize Postdoctoral Fellowship at the California Institute of Technology. The authors thank the Observatoire de la Côte d’Azur for support.

Publisher Copyright:
© 2020 The Author(s).

Keywords

  • Gravitational waves

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