Spin orientations of merging black holes formed from the evolution of stellar binaries

Abstract

We study the expected spin misalignments of merging binary black holes (BHs) formed in isolation by combining state-of-the-art population-synthesis models with efficient post-Newtonian evolutions, thus tracking sources from stellar formation to gravitational-wave detection. We present extensive predictions of the properties of sources detectable by both current and future interferometers. We account for the fact that detectors are more sensitive to spinning BH binaries with suitable spin orientations and find that this significantly impacts the population of sources detectable by LIGO, while this is not the case for 3rd-generation detectors. We find that three formation pathways, differentiated by the order of core collapse and common-envelope phases, dominate the observed population, and that their relative importance critically depends on the recoils imparted to BHs at birth. Our models suggest that measurements of the "effective-spin" parameter ${\chi }_{\mathrm{eff}}$ will allow for powerful constraints. For instance, we find that the role of spin magnitudes and spin directions in ${\chi }_{\mathrm{eff}}$ can be largely disentangled, and that the symmetry of the effective-spin distribution is a robust indicator of the binary's formation history. Our predictions for individual spin directions and their precessional morphologies confirm and extend early toy models, while exploring substantially more realistic and broader sets of initial conditions. Our main conclusion is that specific subpopulations of BH binaries will exhibit distinctive precessional dynamics: these classes include (but are not limited to) sources where stellar tidal interactions act on sufficiently short timescales, and massive binaries produced in pulsational pair-instability supernovae. Measurements of BH spin orientations have enormous potential to constrain specific evolutionary processes in the lives of massive binary stars.

21 pages, 16 figures. Database and python code available at https://github.com/dgerosa/spops - Published in PRD

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