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  1. ; ; ; ( , Physical Review Letters)
    Free, publicly-accessible full text available November 1, 2022
  2. ; ; ; ; ( , Physical Review D)
    Free, publicly-accessible full text available September 1, 2022
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Monthly Notices of the Royal Astronomical Society)
    ABSTRACT Searches for gravitational-wave counterparts have been going in earnest since GW170817 and the discovery of AT2017gfo. Since then, the lack of detection of other optical counterparts connected to binary neutron star or black hole–neutron star candidates has highlighted the need for a better discrimination criterion to support this effort. At the moment, low-latency gravitational-wave alerts contain preliminary information about binary properties and hence whether a detected binary might have an electromagnetic counterpart. The current alert method is a classifier that estimates the probability that there is a debris disc outside the black hole created during the merger as wellmore »as the probability of a signal being a binary neutron star, a black hole–neutron star, a binary black hole, or of terrestrial origin. In this work, we expand upon this approach to both predict the ejecta properties and provide contours of potential light curves for these events, in order to improve the follow-up observation strategy. The various sources of uncertainty are discussed, and we conclude that our ignorance about the ejecta composition and the insufficient constraint of the binary parameters by low-latency pipelines represent the main limitations. To validate the method, we test our approach on real events from the second and third Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO)–Virgo observing runs.« less
    Free, publicly-accessible full text available June 23, 2022
  4. ; ; ; ; ; ; ; ; ( , The Astrophysical Journal)
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  5. ; ; ; ; ( , Machine Learning: Science and Technology)
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  6. ; ; ; ; ( , Physical Review C)
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  7. ; ; ( , The Astrophysical Journal)
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  8. ; ; ; ; ( , The Astrophysical Journal)
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  9. ; ; ( , Physical Review D)
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Machine Learning: Science and Technology)
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