Prospects for optical detections from binary neutron star mergers with the next-generation multi-messenger observatories
E. Loffredo, N. Hazra, U. Dupletsa, M. Branchesi, S. Ronchini, F., Santoliquido, A. Perego, B. Banerjee, S. Bisero, G. Ricigliano, S. Vergani,, I. Andreoni, M. Cantiello, J. Harms, M. Mapelli, G. Oganesyan

TL;DR
Next-generation gravitational wave and optical observatories will significantly enhance detection rates of kilonovae from binary neutron star mergers, with ET alone detecting up to a hundred per year, greatly advancing multi-messenger astronomy.
Contribution
This study models optical signals from BNS mergers considering various population properties and detector configurations, providing updated detection rate estimates for next-generation observatories.
Findings
ET can detect 10-100 kilonovae annually with Rubin Observatory.
Network of GW detectors increases detection rates by a factor of 10.
Detection uncertainties mainly stem from local BNS merger rate estimates.
Abstract
Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and Cosmic Explorer, will observe binary neutron star (BNS) mergers across cosmic history, providing precise parameter estimates for the closest ones. Innovative wide-field observatories, such as the Vera Rubin Observatory, will quickly cover large portions of the sky with unprecedented sensitivity to detect faint transients. This study aims to assess the prospects for detecting optical emissions from BNS mergers with next-generation detectors, considering how uncertainties in neutron star (NS) population properties and microphysics may affect detection rates. Starting from BNS merger populations exploiting different NS mass distributions and equations of state (EOSs), we model the GW and kilonova (KN) signals based on source properties. We model KN ejecta through numerical-relativity informed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
