[论文解读] SEOBNRv5PHM: Next generation of accurate and efficient multipolar precessing-spin effective-one-body waveforms for binary black holes

A. Ramos-Buades, Alessandra Buonanno+6 more|arXiv (Cornell University)|Mar 31, 2023

Pulsars and Gravitational Waves Research被引用 8

一句话总结

SEOBNRv5PHM 提供一款计算高效的多极性翻转自旋 EOB 波形模型，用于全段 BBH 信号，并与大量 NR 数据进行验证，与领先模型具有竞争力。

ABSTRACT

Spin precession is one of the key physical effects that could unveil the origin of the compact binaries detected by ground- and space-based gravitational-wave (GW) detectors, and shed light on their possible formation channels. Efficiently and accurately modeling the GW signals emitted by these systems is crucial to extract their properties. Here, we present SEOBNRv5PHM, a multipolar precessing-spin waveform model within the effective-one-body (EOB) formalism for the full signal (i.e. inspiral, merger and ringdown) of binary black holes (BBHs). In the non-precessing limit, the model reduces to SEOBNRv5HM, which is calibrated to $442$ numerical-relativity (NR) simulations, 13 waveforms from BH perturbation theory, and non-spinning energy flux from second-order gravitational self-force theory. We remark that SEOBNRv5PHM is not calibrated to precessing-spin NR waveforms from the Simulating eXtreme Spacetimes Collaboration. We validate SEOBNRv5PHM by computing the unfaithfulness against 1543 precessing-spin NR waveforms, and find that for 99.8% (84.4%) of the cases, the maximum value, in the total mass range 20-300 $M_\odot$, is below 3% (1%). These numbers reduce to 95.3% (60.8%) when using the previous version of the SEOBNR family, SEOBNRv4PHM, and to 78.2% (38.3%) when using the state-of-the-art frequency-domain multipolar precessing-spin phenomenological IMRPhenomXPHM model. Due to much better computational efficiency of SEOBNRv5PHM compared to SEOBNRv4PHM, we are also able to perform extensive Bayesian parameter estimation on synthetic signals and GW events observed by LIGO-Virgo detectors. We show that SEOBNRv5PHM can be used as a standard tool for inference analyses to extract astrophysical and cosmological information of large catalogues of BBHs.

研究动机与目标

• Motivate accurate modeling of spin-precessing and higher-multipole gravitational waves from binary black holes to inform formation channels and cosmology.
• Develop a computationally efficient precessing-spin EOB waveform model that covers inspiral, merger, and ringdown.
• Calibrate and validate the model against NR data and compare with other state-of-the-art models.

提出的方法

• Construct a multipolar precessing-spin EOB Hamiltonian H_EOB^{pprec} that reduces to SEOBNRv5HM in the aligned-spin limit.
• Use orbit-averaged, PN-expanded spin-precession equations including higher PN orders.
• Evolve dynamics in a co-precessing frame with partial precessional effects, and project spins onto l_N for waveform construction.
• Generate inspiral-plunge waveforms from factorized, resummed PN modes in the co-precessing frame for selected multipoles.
• Attach merger-ringdown using the SEOBNRv5HM non-precessing template with a phenomenological post-merger rotation prescription.
• Represent inertial-frame waveforms through frame rotations among source, J_f, and co-precessing frames using quaternions and Euler angles.

实验结果

研究问题

• RQ1 How accurately can a precessing-spin, multipolar BBH waveform model reproduce NR waveforms across a range of masses, spins, and mass ratios?
• RQ2 What are the performance gains in accuracy and speed of SEOBNRv5PHM relative to SEOBNRv4PHM and PhenomXPHM?
• RQ3 Can the model be efficiently integrated into standard Bayesian inference workflows for LVK-like analyses?
• RQ4 How do higher multipoles and spin-precession affect parameter estimation and potential cosmological inferences?

主要发现

• SEOBNRv5PHM achieves 99.8% of precessing-spin NR cases with unfaithfulness below 3% over total masses 20–300 M_sun.
• The same metric is 84.4% below 1% unfaithfulness for the 99.8% subset.
• Compared to SEOBNRv4PHM, the fraction of cases with unfaithfulness below 3% increases to 95.3% and below 1% to 60.8%.
• Compared to IMRPhenomXPHM, SEOBNRv5PHM yields 78.2% below 3% and 38.3% below 1% unfaithfulness in the same ranges.
• SEOBNRv5PHM is ~8–20 times faster than SEOBNRv4PHM, enabling standard Bayesian inference workflows with LVK-like data.
• Bayesian analyses on synthetic NR signals and LVK events show SEOBNRv5PHM recovers injected signals accurately and yields tighter posteriors than SEOBNRv4PHM.