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arXiv 1001.5380gr-qc
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用遗传算法在模拟LISA数据中搜索自旋黑洞双星

The search for spinning black hole binaries in mock LISA data using a genetic algorithm

Antoine Petiteau, Yu Shang, Stanislav Babak, Farhan Feroz

摘要:

合并的大质量黑洞双星是LISA波段最强的,也可能是最重要的引力波源。自旋和轨道进动带来了波形的复杂性,并使似然曲面的结构比非自旋情况更丰富。我们引入了一种扩展的多模态遗传算法,它利用信号和检测器响应函数的属性来分析来自第三轮模拟LISA数据挑战(MLDC 3.2)的数据。该方法的性能与现有算法相当,如果不是更好的话。我们已经找到了MLDC 3.2中存在的所有五个源,并以合理的精度恢复了合并时间,啁啾质量,质量比和天空位置。对于黑洞的轨道角动量和两个自旋,我们在参数空间中发现了大量具有相似极大似然值的分离模式。

英文摘要:

Coalescing massive Black Hole binaries are the strongest and probably the most important gravitational wave sources in the LISA band. The spin and orbital precessions bring complexity in the waveform and make the likelihood surface richer in structure as compared to the non-spinning case. We introduce an extended multimodal genetic algorithm which utilizes the properties of the signal and the detector response function to analyze the data from the third round of mock LISA data challenge (MLDC 3.2). The performance of this method is comparable, if not better, to already existing algorithms. We have found all five sources present in MLDC 3.2 and recovered the coalescence time, chirp mass, mass ratio and sky location with reasonable accuracy. As for the orbital angular momentum and two spins of the Black Holes, we have found a large number of widely separated modes in the parameter space with similar maximum likelihood values.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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