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Frequency-domain reduced-order model of aligned-spin effective-one-body waveforms with higher-order modes

机译:具有高阶模式的对齐自旋有效一体波形的频域缩小阶模型

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We present a frequency-domain reduced-order model (ROM) for the aligned-spin effective-one-body model for binary black holes (BBHs) seobnrv4hm that includes the spherical-harmonics modes ( ? , | m | ) = ( 2 , 1 ) , ( 3 , 3 ) , ( 4 , 4 ) , ( 5 , 5 ) beyond the dominant ( ? , | m | ) = ( 2 , 2 ) mode. These higher modes are crucial to accurately represent the waveform emitted from asymmetric BBHs. We discuss a decomposition of the waveform, extending other methods in the literature, that allows us to accurately and efficiently capture the morphology of higher mode waveforms. We show that the ROM is very accurate with median (maximum) values of the unfaithfulness against seobnrv4hm lower than 0.001 % ( 0.03 % ) for total masses in [ 2.8 , 100 ] M ⊙ . For a total mass of M = 300 ? ? M ⊙ , the median (maximum) value of the unfaithfulness increases up to 0.004 % ( 0.17 % ) . This is still at least an order of magnitude lower than the estimated accuracy of seobnrv4hm compared to numerical relativity simulations. The ROM is 2 orders of magnitude faster in generating a waveform compared to seobnrv4hm. Data analysis applications typically require O ( 10 6 – 10 8 ) waveform evaluations for which seobnrv4hm is in general too slow. The ROM is therefore crucial to allow the seobnrv4hm waveform to be used in searches and Bayesian parameter inference. We present a targeted parameter estimation study that shows the improvements in measuring binary parameters when using waveforms that includes higher modes and compare against three other waveform models.
机译:我们为二进制黑洞(BBHS)Seobnrv4hm的对齐 - 旋转有效单体模型提供了一种频域缩小阶模型(ROM),包括球形 - 谐波模式(?| m |)=(2, 1),(3,3),(4,4),(5,5)超出优势(?,| m |)=(2,2)模式。这些较高模式至关重要,可以准确地表示不对称BBHS发出的波形。我们讨论了波形的分解,在文献中延伸了其他方法,使我们能够准确和有效地捕获更高模式波形的形态。我们表明,ROM非常准确,在[2.8,100] m⊙中的总质量低于0.001%(0.03%),对Seobnrv4hm的中位数(最大)值。总质量为m = 300?还M⊙,不牢性的中位数(最大)值增加到0.004%(0.17%)。与数值相对论仿真相比,这仍然至少比Seobnrv4hm的估计精度低的数量级。与Seobnrv4hm相比,在生成波形时,ROM是2次数量级。数据分析应用程序通常需要o(10 6 - 10 8)波形评估,其中Seobnrv4hm通常太慢。因此,ROM对于允许SEOBNRV4HM波形以搜索和贝叶斯参数推断使用至关重要。我们介绍了一个有针对性的参数估计研究,其示出了当使用包括更高模式的波形并与三个其他波形模型进行比较时测量二进制参数的改进。

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