论文标题
在一般相对性之外的黑洞合并数值模拟的重力波推理上
Gravitational wave inference on a numerical-relativity simulation of a black hole merger beyond general relativity
论文作者
论文摘要
我们将常见的重力波推理程序应用于二进制黑洞合并波形以外的一般相对论。我们考虑动态的Chern-Simons重力,这是一种重力理论,其起源于字符串理论和环量子重力。该理论引入了一个附加的参数$ \ ell $,对应于长度尺度上,而超过一般性依次效应变得重要。我们基于在该理论近似下产生的数值相对性波形模拟数据,这与强烈非线性合并制度中的一般相对性不同。我们考虑一个类似于GW150914的参数的系统,其值不同,$ \ ell $和信噪比。我们对模拟数据进行了两个分析。第一个是基于模板的分析,该分析使用了在一般相对性下得出的波形,并允许我们识别两个波形形态之间的脱合性。第二个是基于贝叶斯波的形态独立的分析,它不认为信号与一般相对论一致。贝叶斯波分析忠实地重建了模拟信号。但是,在一般相对论下得出的波形模型无法完全模仿模拟的修改性重力信号,并且使用现有推理工具可以识别这种偏差。根据偏差的幅度,我们发现模板分析可以在完全恢复模拟超过GR波形的超过GR波形的情况下执行与形态无关的分析,即使对于可实现的信噪比$ \ gtrsim 20 { - } 30 $。
We apply common gravitational wave inference procedures on binary black hole merger waveforms beyond general relativity. We consider dynamical Chern-Simons gravity, a modified theory of gravity with origins in string theory and loop quantum gravity. This theory introduces an additional parameter $\ell$, corresponding to the length-scale below which beyond-general-relativity effects become important. We simulate data based on numerical relativity waveforms produced under an approximation to this theory, which differ from those of general relativity in the strongly nonlinear merger regime. We consider a system with parameters similar to GW150914 with different values of $\ell$ and signal-to-noise ratios. We perform two analyses of the simulated data. The first is a template-based analysis that uses waveforms derived under general relativity and allows us to identify degeneracies between the two waveform morphologies. The second is a morphology-independent analysis based on BayesWave that does not assume that the signal is consistent with general relativity. The BayesWave analysis faithfully reconstructs the simulated signals. However, waveform models derived under general relativity are unable to fully mimic the simulated modified-gravity signals and such a deviation would be identifiable with existing inference tools. Depending on the magnitude of the deviation, we find that the templated analysis can under perform the morphology-independent analysis in fully recovering simulated beyond-GR waveforms even for achievable signal-to-noise ratios $\gtrsim 20{-}30$.