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arXiv 1503.05405gr-qcastro-ph.HE
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用合并双星中子星的引力波信号约束中子星状态方程

Constraining the neutron star equation of state with gravitational wave signals from coalescing binary neutron stars

Michalis Agathos, Jeroen Meidam, Walter Del Pozzo, Tjonnie G. F. Li, Marco Tompitak, John Veitch, Salvatore Vitale, Chris Van Den Broeck

摘要:

最近进行了探索性研究,研究使用来自聚结中子星或中子星-黑洞系统的信号约束中子星状态方程(EOS)的可能性,因为它们将在即将到来的高级引力波探测器(如Advanced LIGO和Advanced Virgo)中看到。特别是,它估计在多大程度上的组合信息从多个检测将使人们能够区分不同的状态方程通过假设排名或参数估计。假设信号和模板波形中的中子星自旋均为零,并考虑到后牛顿(1 PN)阶的潮汐效应,发现O(20)源足以区分硬、中、软状态方程。在这里,我们重新审视这些结果,这一次包括中子星潮汐效应的最高阶目前已知的,在接触频率,中子星自旋的引力波形的终止,以及由此产生的四极子相互作用。我们还采取了质量的中子星在模拟源分布根据一个相对较强的峰值高斯,暗示的观察,但没有假设,数据分析师将有必要准确的知识,这种分布作为一个质量先验。我们发现,特别是后者的效果是戏剧性的,需要更多的检测来区分不同的EOS和参数估计造成系统性偏差,在偏见之上,由于不完善的理解的信号模型在早期的工作中指出。如果能将质量分布的可靠先验信息纳入分析,这一问题将得到缓解。

英文摘要:

Recently exploratory studies were performed on the possibility of constraining the neutron star equation of state (EOS) using signals from coalescing binary neutron stars, or neutron star-black hole systems, as they will be seen in upcoming advanced gravitational wave detectors such as Advanced LIGO and Advanced Virgo. In particular, it was estimated to what extent the combined information from multiple detections would enable one to distinguish between different equations of state through hypothesis ranking or parameter estimation. Under the assumption of zero neutron star spins both in signals and in template waveforms and considering tidal effects to 1 post-Newtonian (1PN) order, it was found that O(20) sources would suffice to distinguish between a hard, moderate, and soft equation of state. Here we revisit these results, this time including neutron star tidal effects to the highest order currently known, termination of gravitational waveforms at the contact frequency, neutron star spins, and the resulting quadrupole-monopole interaction. We also take the masses of neutron stars in simulated sources to be distributed according to a relatively strongly peaked Gaussian, as hinted at by observations, but without assuming that the data analyst will necessarily have accurate knowledge of this distribution for use as a mass prior. We find that especially the effect of the latter is dramatic, necessitating many more detections to distinguish between different EOS and causing systematic biases in parameter estimation, on top of biases due to imperfect understanding of the signal model pointed out in earlier work. This would get mitigated if reliable prior information about the mass distribution could be folded into the analyses.

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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