paper

Interplay of spin-precession and higher harmonics in the parameter estimation of binary black holes

arXiv:2110.00766 · doi:10.1103/PhysRevD.105.064012

Abstract

Gravitational-wave (GW) signals from coalescing compact binaries carry enormous information about the source dynamics and are an excellent tool to probe unknown astrophysics and fundamental physics. Though the updated catalog of compact binary signals reports evidence for slowly spinning systems and unequal mass binaries, the data so far cannot provide convincing proof of strongly precessing binaries. Here, we use the GW inference library parallel Bilby to compare the performance of two waveform models for measuring spin-induced orbital precession. One of the waveform models incorporates both spin-precession effects and sub-dominant harmonics. The other model accounts for precession but only includes the leading harmonic. By simulating signals with varying mass ratios and spins, we find that the waveform model with sub-dominant harmonics enables us to infer the presence of precession in most cases accurately. In contrast, the dominant model often fails to extract enough information to measure precession. In particular, it cannot distinguish a face-on highly precessing binary from a slowly precessing binary system irrespective of the binary's mass ratio. As expected, we see a significant improvement in measuring precession for edge-on binaries. Other intrinsic parameters also become better constrained, indicating that precession effects help break the correlations between mass and spin parameters. However, the precession measurements are prior dominated for equal-mass binaries with face-on orientation, even if we employ waveform model including subdominant harmonics. In this case, doubling the signal-to-noise ratio does not help to reduce these prior induced biases. As we expect detections of highly spinning binary signals with misaligned spin orientations in the future, simulation studies like ours are crucial for understanding the prospects and limitations of GW parameter inferences.

arXiv comment12 pages, 8 figures Data release URL: https://zenodo.org/record/6337692#.YieS5BDMKLp

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