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The spin orientations of spinning binary black hole (BBH) mergers detected by ground-based gravitational wave detectors such as LIGO and Virgo can provide important clues about the formation of such binaries. However, these spin tilts, i.e., the angles between the spin vector of each black hole and the binary’s orbital angular momentum vector, can change due to precessional effects as the black holes evolve from a large separation to their merger. The tilts inferred at a frequency in the sensitive band of the detectors by comparing the signal with theoretical waveforms can thus be significantly different from the tilts when the binary originally formed. These tilts at the binary’s formation are well approximated in many scenarios by evolving the BBH backward in time to a formally infinite separation. Using the tilts at infinite separation also places all binaries on an equal footing in analyzing their population properties. In this paper, we perform parameter estimation for simulated BBHs and investigate the differences between the tilts one infers directly close to merger and those obtained by evolving back to infinite separation. We select simulated observations such that their configurations show particularly large differences in their orientations close to merger and at infinity. While these differences may be buried in the statistical noise for current detections, we show that in future plus-era (A+ and Virgo+) detectors, they can be easily distinguished in some cases. We also consider the tilts at infinity for BBHs in various spin morphologies and at the endpoint of the up-down instability. In particular, we find that we are able to easily identify the up-down instability cases as such from the tilts at infinity.
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Distinguishing binary black hole precessional morphologies with gravitational wave observations
The precessional motion of binary black holes can be classified into one of three morphologies, based on the evolution of the angle between the components of the spins in the orbital plane: Circulating, librating around 0, and librating around π. These different morphologies can be related to the binary’s formation channel and are imprinted in the binary’s gravitational wave signal. In this paper, we develop a Bayesian model selection method to determine the preferred spin morphology of a detected binary black hole. The method involves a fast calculation of the morphology which allows us to restrict to a specific morphology in the Bayesian stochastic sampling. We investigate the prospects for distinguishing between the different morphologies using gravitational waves in the Advanced LIGO/Advanced Virgo network with their plus-era sensitivities. For this, we consider fiducial high- and low-mass binaries having different spin magnitudes and signal-to-noise ratios (SNRs). We find that in the cases with high spin and high SNR, the true morphology is strongly favored with log10 Bayes factors ≳4 compared to both alternative morphologies when the binary’s parameters are not close to the boundary between morphologies. However, when the binary parameters are close to the boundary between morphologies, only one alternative morphology is strongly disfavored. In the low-spin, high-SNR cases, the true morphology is still favored with a log10 Bayes factor ∼2 compared to one alternative morphology, while in the low-SNR cases the log10 Bayes factors are at most ∼1 for many binaries. We also consider the gravitational wave signal from GW200129_065458 that has some evidence for precession (modulo data quality issues) and find that there is no preference for a specific morphology. Our method for restricting the prior to a given morphology is publicly available through an easy-to-use python package called bbh_spin_morphology_prior.
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- PAR ID:
- 10496766
- Publisher / Repository:
- American Physical Society
- Date Published:
- Journal Name:
- Physical Review D
- Volume:
- 108
- Issue:
- 10
- ISSN:
- 2470-0010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1103/PhysRevD.108.103003
