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arXiv 2006.06256astro-ph.HE
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非零偏心率二元黑洞引力波信号的贝叶斯重建

Bayesian Reconstruction of Gravitational-wave Signals from Binary Black Holes with Nonzero Eccentricities

Gergely Dálya, Peter Raffai, Bence Bécsy

摘要:

我们提出了一个全面的研究如何以及引力波信号的二元黑洞与非零偏心率可以恢复与国家的最先进的模型无关的波形重建和参数估计技术。为此,我们使用BayesWave,这是LIGO-Virgo协作组用于信号波形和参数的未建模重建的贝叶斯算法。我们使用两种不同的波形模型来产生具有偏心轨道的双黑洞的模拟信号,并将它们嵌入到设计灵敏度的Advanced LIGO探测器的模拟噪声样本中。我们研究了网络重叠和点估计的信号波形恢复贝叶斯波作为$e$,偏心率的二进制在8 Hz的轨道频率的函数的中心时刻。BayesWave恢复近圆形($e\lesssim0.2$)和高度偏心($e\gtrsim0.7$)二进制信号,其网络重叠类似于圆形($e=0$)二进制信号,但它对具有$e\in[0.2,0.7]$的二进制信号产生较低的网络重叠。中心频率和带宽(相对于带宽测量)上的估计误差几乎与$e$无关,而中心时间和持续时间(相对于持续时间测量)上的估计误差分别随着$e$高于$e\gtrsim0.5$而增加和减少。我们还测试了BayesWave在使用具有线性频率演化的广义小波(chirplets)而不是正弦高斯小波进行重建时的表现。我们已经发现,当使用线性调频波时,网络重叠改善了$\sim 10-20$百分比,并且在低($e<0.5$)偏心率下改善最高。然而,当使用线调频基时,中心矩的估计误差没有显着变化。

英文摘要:

We present a comprehensive study on how well gravitational-wave signals of binary black holes with nonzero eccentricities can be recovered with state of the art model-independent waveform reconstruction and parameter estimation techniques. For this we use BayesWave, a Bayesian algorithm used by the LIGO-Virgo Collaboration for unmodeled reconstructions of signal waveforms and parameters. We used two different waveform models to produce simulated signals of binary black holes with eccentric orbits and embed them in samples of simulated noise of design-sensitivity Advanced LIGO detectors. We studied the network overlaps and point estimates of central moments of signal waveforms recovered by BayesWave as a function of $e$, the eccentricity of the binary at 8 Hz orbital frequency. BayesWave recovers signals of near-circular ($e\lesssim0.2$) and highly eccentric ($e\gtrsim0.7$) binaries with network overlaps similar to that of circular ($e=0$) ones, however it produces lower network overlaps for binaries with $e\in[0.2,0.7]$. Estimation errors on central frequencies and bandwidths (measured relative to bandwidths) are nearly independent from $e$, while estimation errors on central times and durations (measured relative to durations) increase and decrease with $e$ above $e\gtrsim0.5$, respectively. We also tested how BayesWave performs when reconstructions are carried out using generalized wavelets with linear frequency evolution (chirplets) instead of sine-Gaussian wavelets. We have found that network overlaps improve by $\sim 10-20$ percent when chirplets are used, and the improvement is the highest at low ($e<0.5$) eccentricities. There is however no significant change in the estimation errors of central moments when the chirplet base is used.

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