Systematic biases in parameter estimation of binary black-hole mergers

被引:49
作者
Littenberg, Tyson B. [1 ,2 ,3 ]
Baker, John G. [3 ]
Buonanno, Alessandra [1 ,2 ]
Kelly, Bernard J. [3 ,4 ,5 ]
机构
[1] Univ Maryland, Maryland Ctr Fundamental Phys, Dept Phys, College Pk, MD 20742 USA
[2] Univ Maryland, Joint Space Sci Inst, College Pk, MD 20742 USA
[3] NASA, Goddard Space Flight Ctr, Gravitat Astrophys Lab, Greenbelt, MD 20771 USA
[4] Univ Maryland, Dept Phys, Baltimore, MD 21250 USA
[5] NASA, Goddard Space Flight Ctr, CRESST, Greenbelt, MD 20771 USA
来源
PHYSICAL REVIEW D | 2013年 / 87卷 / 10期
基金
美国国家科学基金会;
关键词
GRAVITATIONAL-RADIATION; COMPACT-OBJECT; MASS; X-1;
D O I
10.1103/PhysRevD.87.104003
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Parameter estimation of binary black-hole merger events in gravitational-wave data relies on matched-filtering techniques which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing nonspinning numerical-relativity waveforms. We quantify the systematic bias by using a Markov chain Monte Carlo algorithm to sample the posterior distribution function of noise-free data, and compare the offset of the maximum a priori waveform parameters (the bias) to the width of the distribution, which we refer to as the statistical error. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios. These biases grow to be comparable to the statistical errors at high ground-based-instrument signal-to-noise ratios (SNR similar to 50), but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors, but for astrophysical black hole mass estimates the absolute biases (of at most a few percent) are still fairly small.
引用
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页数:16
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