English

Investigating the Cosmological Rate of Compact Object Mergers from Isolated Massive Binary Stars

High Energy Astrophysical Phenomena 2024-05-06 v1

Abstract

Gravitational wave detectors are observing compact object mergers from increasingly far distances, revealing the redshift evolution of the binary black hole (BBH) -- and soon the black hole-neutron star (BHNS) and binary neutron star (BNS) -- merger rate. To help interpret these observations, we investigate the expected redshift evolution of the compact object merger rate from the isolated binary evolution channel. We present a publicly available catalog of compact object mergers and their accompanying cosmological merger rates from population synthesis simulations conducted with the COMPAS software. To explore the impact of uncertainties in stellar and binary evolution, our simulations use two-parameter grids of binary evolution models that vary the common-envelope efficiency with mass transfer accretion efficiency, and supernova remnant mass prescription with supernova natal kick velocity, respectively. We quantify the redshift evolution of our simulated merger rates using the local (z0z\sim 0) rate, the redshift at which the merger rate peaks, and the normalized differential rates (as a proxy for slope). We find that although the local rates span a range of 103\sim 10^3 across our model variations, their redshift-evolutions are remarkably similar for BBHs, BHNSs, and BNSs, with differentials typically within a factor 33 and peaks of z1.22.4z\approx 1.2-2.4 across models. Furthermore, several trends in our simulated rates are correlated with the model parameters we explore. We conclude that future observations of the redshift evolution of the compact object merger rate can help constrain binary models for stellar evolution and gravitational-wave formation channels.

Keywords

Cite

@article{arxiv.2405.01630,
  title  = {Investigating the Cosmological Rate of Compact Object Mergers from Isolated Massive Binary Stars},
  author = {Adam Boesky and Floor S. Broekgaarden and Edo Berger},
  journal= {arXiv preprint arXiv:2405.01630},
  year   = {2024}
}

Comments

16 pages, 9 figures, link to code https://github.com/Adam-Boesky/Exploring_Parameter_Space, link to data https://gwlandscape.org.au/compas/publication/Q29tcGFzUHVibGljYXRpb25Ob2RlOjYz/