Binary Black Hole Mergers: Spin and mass ratio effects on gravitational waveforms

Binary Black Hole Mergers: Spin and mass ratio effects on gravitational waveforms
Notice: This research summary and analysis were automatically generated using AI technology. For absolute accuracy, please refer to the [Original Paper Viewer] below or the Original ArXiv Source.

We present a comprehensive parameter-space study of binary black hole (BBH) mergers using the SEOBNRv4_opt waveform model. Our analysis spans $\sim 10^6$ simulated waveforms across a broad range of mass ratios ( q = \frac{m_1}{m_2} \in [1.0, 2.0] ) and aligned spin configurations. We investigate the influence of these parameters on remnant properties, including final spin ($χ_f$), fractional mass loss ($M_{\mathrm{FL}}$), and peak gravitational-wave strain ($h_{\max}$). By systematically analyzing the trends across four distinct spin alignments (PP, PN, BP, BN), we identify non-monotonic behaviors and turning points in $M_{\mathrm{FL}}$ and $χ_f$ as functions of $q$, highlighting subtle dynamical effects that are not explicitly emphasized in commonly used remnant fitting formulae. While confirming known correlations from numerical relativity, our results offer new insights into parameter interactions and waveform morphology, with implications for BBH population studies and remnant characterization. Across all configurations studied, the fractional mass loss due to gravitational-wave emission ranges between 2% and 9.5%, depending on the mass ratio and spin alignment. This work may also aid in understanding the spin and mass distributions of the more massive black holes formed post-merger, thereby contributing to future remnant-based astrophysical inference.


💡 Research Summary

This paper presents a systematic, high‑resolution exploration of how the binary black‑hole (BBH) mass ratio (q = m₁/m₂) and aligned spin configurations affect gravitational‑wave (GW) waveforms and the properties of the merger remnant. Using the state‑of‑the‑art effective‑one‑body (EOB) model SEOBNRv4_opt, the authors generate roughly one million waveforms spanning q ∈


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