标题： 通过引力波观测区分双黑洞进动形态 显示英文标题

标题： Distinguishing binary black hole precessional morphologies with gravitational wave observations

摘要： 二元黑洞的进动运动可以根据轨道平面内自旋分量之间的角度演化分为三种形态：循环、围绕0摆动和围绕$\pi$摆动。这些不同的形态与双星的形成通道有关，并且在双星的引力波信号中被记录下来。 在本文中，我们开发了一种贝叶斯模型选择方法来确定检测到的二元黑洞的首选自旋形态。该方法涉及快速计算形态，这使我们能够在贝叶斯随机抽样中限制到特定的形态。我们研究了在高级LIGO/高级Virgo网络的灵敏度下，利用引力波区分不同形态的可能性。为此，我们考虑了具有不同自旋大小和信噪比（SNR）的标称高质量和低质量双星。 我们发现，在高自旋和高SNR的情况下，当双星参数远离形态边界的时，真正确切的形态受到强烈支持，其贝叶斯因子为$\log_{10}$，相比两种替代形态分别高出$\gtrsim 4$。然而，当双星参数接近形态边界时，只有其中一种替代形态被强烈排斥。 在低自旋、高SNR的情况下，真正确切的形态仍然受到支持，其贝叶斯因子为$\log_{10}$，相比一种替代形态高出$\sim 2$。我们还考虑了GW200129_065458事件的引力波信号，该信号显示出一些进动的证据（数据质量问题除外），并发现没有偏好于特定形态。 我们的方法通过一个名为 bbh_spin_morphology_prior 的易于使用的Python软件包公开提供，用于将先验限制到给定的形态。 显示英文摘要

摘要： 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 $\pi$. 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 $\log_{10}$ Bayes factors $\gtrsim 4$ 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 $\log_{10}$ Bayes factor $\sim 2$ compared to one alternative morphology. 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. (Abridged)