论文标题
识别何时可以在重力波形中测量进动
Identifying when Precession can be Measured in Gravitational Waveforms
论文作者
论文摘要
在黑孔旋转与轨道角动量未对准的二进制黑孔系统中,进动效果在发射的引力波形中留下了特征调制。在这里,我们研究了在参数空间中,我们将能够准确地识别进度,以观察到即将到来的Ligo-Virgo-Kagra观察跑步。尽管有大量的参数表征了进攻二进制的参数,但我们仍执行一项大规模的系统研究,以确定每个源参数对进动测量的影响。我们以中等质量比率,信噪比(SNR)和旋转模拟了一个基准二进制,因此可以清楚地识别进积,然后连续地改变每个参数,同时固定固定剩余的参数。正如预期的那样,前进的证据随信噪比(SNR),较高的面内旋转,更不平等的组分质量和较高的倾向增加,但是我们的研究提供了这些效应的定量说明,并为我们对参数依赖的直觉提供了详细研究的直觉,例如,尚未详细研究的质量和质量的效果,构成了两极分化的效果。我们还测量了“预处理SNR” $ρ_P$,该$ρ_p$是在参考文献[1,2]中引入的,以量化与进动相关的信号功率。通过将$ρ_p$与两个贝叶斯因子和回收的后验分布进行比较,我们发现它是可靠的测量性指标,可以准确预测何时检测到的信号含有预动力的证据。
In binary-black-hole systems where the black-hole spins are misaligned with the orbital angular momentum, precession effects leave characteristic modulations in the emitted gravitational waveform. Here, we investigate where in the parameter space we will be able to accurately identify precession, for likely observations over coming LIGO-Virgo-KAGRA observing runs. Despite the large number of parameters that characterise a precessing binary, we perform a large scale systematic study to identify the impact of each source parameter on the measurement of precession. We simulate a fiducial binary at moderate mass-ratio, signal-to-noise ratio (SNR), and spins, such that precession will be clearly identifiable, then successively vary each parameter while holding the remaining parameters fixed. As expected, evidence for precession increases with signal-to noise-ratio (SNR), higher in-plane spins, more unequal component masses, and higher inclination, but our study provides a quantitative illustration of each of these effects, and informs our intuition on parameter dependencies that have not yet been studied in detail, for example, the effect of varying the relative strength of the two polarisations, the total mass, and the aligned-spin components. We also measure the "precession SNR" $ρ_p$, which was introduced in Refs[1,2] to quantify the signal power associated with precession. By comparing $ρ_p$ with both Bayes factors and the recovered posterior distributions, we find it is a reliable metric for measurability that accurately predicts when the detected signal contains evidence for precession.